Military Surveillance Robot with Image Processing

Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.

Autonomous systems operating in dangerous and hard-to-reach environments such as defense systems deployed into enemy territory, petroleum installations running in remote arctic and off-shore environments, or space exploration systems operating on Mars and further out in the solar system often are designed with a wide operating envelope and deployed with control systems that are designed to both protect the system and complete mission objectives, but only when the on-the-ground environment matches the expected and designed for environment. This can lead to overly conservative operating strategies such as preventing a rover on Mars from exploring a scientifically rich area due to potential hazards outside of the original operating envelope and can lead to unanticipated failures such as the loss of underwater autonomous vehicles operating in Earth’s oceans. This paper presents an iterative method that links computer simulation of operations in unknown and dangerous environments with conceptual design of systems and development of control system algorithms. The Global to Local Path Finding Design and Operation Exploration (GLPFDOE) method starts by generating a general mission plan from low resolution environmental information taken from remote sensing data (e.g.: satellites, plane fly-overs, telescope observations, etc.) and then develops a detailed path plan from simulated higher-resolution data collected “in situ” during simulator runs. GLPFDOE attempts to maximize system survivability and scientific or other mission objective yield through iterating on control system algorithms and system design within an in-house-developed physics-based autonomous vehicle and terrain simulator. GLPFDOE is best suited for autonomous systems that cannot have easy human intervention during operations such as in the case of robotic exploration reaching deeper into space where communications delays become unacceptably large and the quality of a priori knowledge of the environment becomes lower fidelity. Additionally, in unknown extraterrestrial environments, a variety of unexpected hazards will be encountered that must to be avoided and areas of scientific interest will be found that must be explored. Existing exploratory platforms such as the Mars Exploratory Rovers (MERs) Curiosity and Opportunity either operate in environments that are sufficiently removed from immediate danger or take actions slowly enough that the signal delay between the system and Earth-based operators is not too great to allow for human intervention in hazardous scenarios. Using the GLPFDOE methodology, an autonomous exploratory system can be developed that may have a higher likelihood of survivability, can accomplish more scientific mission objectives thus increasing scientific yield, and can decrease risk of mission-ending system damage. A case study is presented in which an autonomous Mars Exploration Rover (MER) is generated and then refined in a simulator using the GLPFDOE method. Development of the GLPFDOE methodology allows for the execution of more complex missions by autonomous systems in remote and inaccessible environments.

Border security threats and disaster response requirements have intensified, leading to a rising need for intelligent robotic systems capable of operating in high-risk environments with minimal human intervention. In real-world scenarios such as border surveillance, military reconnaissance, disaster management, and search-and-rescue missions, there is a critical requirement for systems that provide real-time monitoring, predictive analysis, and remote accessibility. These applications demand continuous situational awareness, rapid decision-making, and safe operation in hazardous or inaccessible terrains. Traditional surveillance methods rely heavily on human personnel and static monitoring systems, which are limited by restricted mobility, delayed response times, and high risk to human life. Furthermore, conventional systems lack intelligent data processing, predictive capabilities, and seamless integration of multi-sensor information, reducing their effectiveness in dynamic environments. To overcome these limitations, the proposed war field spy robot integrates Artificial Intelligence (AI) and Internet of Things (IoT) technologies to deliver an advanced, autonomous surveillance platform. The system employs high-definition cameras, GPS modules, and IoT-based sensors to capture real-time video and environmental data, which are transmitted via wireless networks to a remote-control station. AI-driven algorithms enable automatic object detection, tracking, and classification, while predictive location analysis forecasts potential threats and movement patterns. The robot operates in both remote-controlled and semi-autonomous modes, ensuring flexibility and mission efficiency. This intelligent integration enhances operational safety, reduces human risk, and provides a scalable, reliable solution for modern defense and security applications.

Purpose– As an extended work of the previous paper (Sul, 2010), this paper provides a guideline information for an anonymous garment pattern in sewing process. The purpose of this paper is to first, provide garment pattern database. By simply taking pictures of garment patterns, the shape database is constructed. Once the shape database is prepared, data retrieval can be done by image indexing, i.e., simply inserting garment pattern boundary shape again to the database. Using shock graph methodology, the pattern sets used for database preparation can be exactly retrieved. Second, to find the nearest shape of a given input pattern shape in the database. If the input garment pattern shape does not exist in the database, the shape matching algorithm provides the next similar pattern data. The user, who is assumed to be non-expert in garment sewing process, can easily predict the position and combination information of various patterns.Design/methodology/approach– Image processing is used to construct the garment pattern shape database. The boundary shapes are extracted from the photographs of garment patterns and their shape recognition information, especially shock graph, is also recorded for later pattern data retrieval.Findings– Using the image processing technique, garment patterns can be converted to electronic format easily. Also the prepared pattern database can be used for finding the nearest shape of an additional given input garment pattern. Patterns with irregular shapes were retrieved easily, while those with a simple shape, such as rectangle, showed a little erroneous result.Originality/value– Shape recognition has been adopted in various industrial areas, except for garment sewing process. Using the provided methodology, garment pattern shapes can be easily saved and retrieved only by taking pictures of them.

Background subtraction is important process in many automatic video content analysis applications.extracting foreground moving objects from video sequences is a crucial step and also a hot topic in computer vision and image processing .This paper presents a new approach in image processing for detection crack in video.This method involves two main steps: First, we use based on Visual Background Extractor (Vibe) to detection moving crack in video.Second steps: Using a suitable threshold in a binary image and classifies all pixels two groups' background and foreground.And use filter area and changes the area if less than the specific number to back; and using filtering to elimination of residual noise.This paper describes a method for detection crack in video we use Image Processing Techniques.The advantage of this method is clearly and accurate detection of cracks in video.Experimental work shows that our method is improved relatively to the other widely used techniques.

Highlights AI can enhance agricultural safety through predictive models, LLMs, computer vision, and wearables. Machine learning is a tool that has the potential to predict hazards using historical data on injury incidents, weather, and worker behavior. Computer vision and remote sensing applications could be used to detect unsafe conditions for real-time risk mitigation. Wearable AI devices present opportunities to monitor worker health and prevent injuries in agricultural environments. ABSTRACT. Artificial intelligence (AI) has emerged as a transformative tool in various industries, including agriculture, where it has the potential to enhance safety and reduce injury risks. This review explores the application of AI techniques in occupational safety with a special focus on agricultural safety, focusing on predictive modeling, large language models (LLMs), computer vision, and wearable technologies. Due to the limited number of studies that address AI in agricultural safety, research from related fields, such as construction safety, was also considered for its potential applicability. The findings indicate that (1) predictive models that leverage machine learning (ML) algorithms can assess historical data and forecast hazards, enabling proactive safety measures. (2) LLMs can improve injury report analysis by extracting key terms and patterns to identify recurring risks. (3) Computer vision and remote sensing technologies enhance environmental monitoring by detecting real-time unsafe conditions. At the same time, (4) AI-powered wearable devices can track worker health indicators such as heart rate, potentially preventing injuries. A total of 85 studies were analyzed, providing insights into the diverse applications of AI in mitigating occupational hazards, specifically agricultural hazards. This review highlights the current advancements and future research opportunities for AI-driven safety interventions in high-risk occupational environments. Keywords: Agricultural safety, Artificial intelligence, Computer vision, Health, Injury prevention, Large language models, Machine learning, Occupational safety, Predictive modeling, Wearable technology.

Advanced robots are desired to improve their reasoning ability in increasingly practical situations to quickly respond to urgent challenges such as search and rescue, public security, and autonomous driving. Advances in sensor technology and intelligent algorithms are accelerating the development of autonomous systems, allowing them to interact with dynamic environments more independently and intelligently, completing relatively complicated tasks. Nevertheless, the safety and security problems are yet to be comprehensively addressed. Currently, many efforts have been made to solve this problem by improving the unimodal perception ability of intelligent systems. Methods using surveillance cameras to collect large amounts of visual data and process them using deep networks. Various types of range scanner can sense and reconstruct the surrounding environment through precise distance measurements. However, in the above cases, there may be a lack of training data, fixed cameras cannot adequately and dynamically model the surroundings, unimodal analysis is hard to capture both appearance and depth information. Therefore, higher levels of perception and reasoning abilities are required to handle unexpected conditions and scenarios efficiently. In such cases, mobile robots should be fully aware of the dynamic environment, prioritize, and continuously analyze critical situations, share information, and collaboration under limited computing power and resources. Furthermore, considering the robots are operating alongside humans, reliable and interpretable robot behavior is highly preferred for more natural interactions.
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\nThis study aims to develop reasoning strategies for autonomous robots to improve robotic intelligence under different assumptions and conditions. The framework allows one or several robots to address safety or security problems in a targeted manner in partially known environments, where time, dynamics, and interactions play a significant role. The objective is to identify the unique individual whose activity, in space and time, appear different from the other people in the scene. The activities considered not only the motion pattern, but also the object associated with this person. Unlike traditional abnormal detection, the uniqueness reasoning in this work does not require a predefined expected pattern. A modular analysis is also applied to decompose the entire reasoning process into submodules such as perception, analysis, and inference, to achieve algorithmic flexibility and interpretability. Probabilistic modeling is used to associate spatial relationships between semantic labels and their temporal changes. The relationships are described in either instant spatial spaces (semantic-interaction feature) or spatiotemporal spaces (spatiotemporal feature), and it provides essential information for robot navigation, situation awareness, and task planning. Unlike existing research, this is the first time that such a mission is implemented on mobile robot platforms by using the probabilistic fusion of multimodal information.
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\nConsidering how evidence information is obtained and linked in the process of reasoning, we study the problem of uniqueness reasoning at three levels. The first level is called knowledge-based single robot uniqueness reasoning and runs on a single robot platform. At this level, we focus on a knowledge-based approach, where knowledge is provided in terms of how motion and an object associated with a person can be used to infer his/her uniqueness, as determined by human expertise. Because such a method has advantages in dealing with structured missions and with limited training data. Qualitative rules are applied to extract human activities from observations, and then the uniqueness of each person is judged by associating the prior knowledge with the performed activities. Experiments were conducted in indoor environments with various degrees of occlusion, and the experimental results showed the rationality and accuracy of the reasoning.
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\nHowever, though the knowledge-based method can successfully address specific tasks, it lacks flexibility and generality to adapt to different situations. The assumption of prior knowledge should be replaced by general analytical modeling. Thus, the second level is analytics-based single robot uniqueness reasoning. For one thing, it applies quantitative analysis to model human activities from observations rather than using qualitative rules. For another, instead of leveraging prior knowledge, the distribution of the features characterizing a human activity is constructed and the activity of an individual is analyzed within the distribution to determine if it is unique. Experiments were conducted in both indoor and urban outdoor environments. The experimental results demonstrated the rationality, accuracy, and efficiency of the reasoning.
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\nMoreover, since many applications require to work in an unstructured and large-scale environment as well as a long-term operation, the sensing and reasoning capabilities of a single robot are limited by occlusion, sensor failure, and narrow field of view. Robots are desired to share information and perform collaborative reasoning. Therefore, if say the first two levels are mainly using sensory data to implement the algorithm, the third level is more focused on applying the algorithm to the robot platform. The third level is analytics-based multi-robot uniqueness reasoning, in which a multi-robot system is adopted as the platform. By obtaining the relative pose between robots, local observations can be transformed and transmitted among the multi-robot system. Since it is a distributed system, each robot still first performs its local uniqueness reasoning in the case of long-distance and poor communication. Meanwhile, the system performs a re-judgment based on the shared global observation and then shares the judgment results back to each robot. Experiments were carried out in various unstructured environments and contained day and night situations. The experimental results showed the rationality, accuracy, and robustness of the reasoning. This is the first time that reasoning is categorized by considering the way of obtaining and linking evidence information. This study has potential applications in a variety of situations, and it can serve as a guide for further robotic motion planning and human-robot interaction.

An optical fiber multiplexed image transmission system can be used in dangerous and low accessible environments, such as in nuclear or high explosion risk environments because the optical fiber cable is insensitive to the electromagnetic disturbances and, when an appropriate optical fiber is used, it withstands the gamma radiation. The system has the capability to simultaneously transmit, by using the video signals switch, the image from four CCD cameras placed in the dangerous environment to a remote surveillance center, at 2 km distance.

The human brain is a remarkably complex organ. Comprehension of the tissue properties of the brain and the multitude of changes in disease and ageing may lead to a greater appreciation and understanding of human biology. Combined, Magnetic Resonance Imaging (MRI) and image processing techniques are now capable of resolving very high-resolution images of the brain, including characterising discrete tissue types within small brain structures such as the hippocampus. The hippocampus is a small structure most commonly associated with its function in memory, and is comprised of subfields with distinct cellular compositions (or laminae). Computational techniques for delineating tissue types within the hippocampus incorporate sophisticated computer vision and medical imaging algorithms to better describe the range of changes occurring in these structures. In parallel, improved MR scanner hardware and sequence choices including leveraging multiple contrasts of the same tissue in vivo allows for complementary imaging information of the structure, leading to a more specific and reliable characterisation of brain tissue. Despite advances in the field, several competing algorithms and techniques are used for hippocampus subfield morphometry properties such as volume and shape.This thesis will explore computational techniques for characterising human brain tissue in vivo using multi-contrast, high resolution, ultra high-field MRI data. Specifically, this thesis will focus on characterising the tissue of the hippocampus as an exemplar for the heterogeneity of tissue classes, complex shape changes, and large, variable individual differences in anatomy between healthy and diseased/disordered subjects. This thesis will pay specific attention to characterising hippocampus tissue (in particular, image segmentation and volumetry) in a wide range of subjects and patients, which is often ignored in computational techniques for analysing MRI data.First, techniques for retrospectively ameliorating within-session motion in MRI in both healthy participants and patients with Motor Neurone Disease (MND) will be examined and discussed. It was found that incorporating a computational technique for image registration improved the reliability of image segmentation of the subfields of the hippocampus.Next, in order to better characterise the changes in anatomy over time using MRI, we constructed and evaluated a longitudinal pipeline for segmenting and examining the volume of hippocampal subfields using computational techniques in a healthy participant pool and patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To aid in this research, the Towards Optimising MRI ChAracterisation of Tissue (TOMCAT) data set was acquired using Ultra-High Field (7-Tesla) MRI. Thirdly, to better characterise the subfields of the hippocampus and in less time than traditional computational techniques, a deep learning approach to hippocampus subfield segmentation using multi-contrast, high-resolution MRI was developed and evaluated. We found that deep learning techniques may aid in speeding the process of hippocampus subfield segmentation and deliver accurate results.Finally, the clinical applications of the previous studies were explored. We detail automated methods for extracting precise measures describing hippocampal subfield morphology and compare these features in MND patients and healthy controls. Brain tissue can be successfully characterised in vivo using high resolution and multi-contrast MRI. Developing robust and reproducible methods that are useful in both research and clinical contexts is an important step in demystifying biological processes involved in ageing and contributing to disorder and disease. This thesis adds to and improves the broader knowledge base of computational techniques for characterising brain tissue and adds novel and enhanced methodology to the field of neuroimaging.

The goal of this book was to introduce the reader to the recent advances in the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). Our original motivation was the observation that more and more often, measured data in the form of images are being used as part of the simulation pipeline, and that error propagation and uncertainty quantification—starting from images but then moving through the pipeline—are important to understanding the ending result of this scientific or engineering process. To analyze the image processing pipeline, we have presented concepts that enable error propagation to be formulated with a set of basic operations: the idea of stochastic images and corresponding (numerical PDE) operations on those images. From this perspective, one approach commonly used within the engineering literature is to employ Monte Carlo sampling techniques to attempt to quantify the impact of errors and variability on an engineering pipeline. Within the uncertainty quantification world, alternative approaches such as the class of generalized polynomial chaos (gPC) methods, which make assumptions about the ability to approximate the process of interest through polynomial approximations, have gained traction as a way of accelerating the convergence of the error and uncertainty quantification process in a computationally tractable way. In this book, we have shown the results of our exploration of the use of the gPC methodology for image processing and computer vision problems. We have relied on the fact that all the prerequisites of the gPC framework (e.g., finite variance, smoothness in the stochastic space, etc.) are intrinsically satisfied in the image processing context. In particular, we have shown how the generalized polynomial chaos (gPC) approach and its corresponding rules for computation (e.g., sums, products, projections on the gPC space, etc.) lead to straightforward generalizations to image processing and computer vision techniques. We have shown that the gPC methodology, when combined with image processing and computer vision techniques, provides a powerful approach to error propagation and uncertainty quantification, and to accessing system sensitivity.

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in next-generation wireless communications are discussed.

This work presents the development of a solution for the detection of the position of glass plates and sheets, through computer vision and image processing techniques. This position information is intended to be used in Computer Numeric Control (CNC) waterjet cutting machines, so that the cutting region can be determined. This solution was built using the Python language, using the computer vision library, OpenCV and the mathematical manipulations library, Numpy. To verify the performance of the proposed solution, a test platform was set up, carrying out experiments with glass samples of different types and colors and, through the tests, a minimum error of 0.02mm was detected in relation to the reference position. Therefore, it is expected through this research to contribute to the automation of the entire process of cutting glass plates and sheets.

Plants are the world's most significant resource since they are the only natural source of oxygen. Additionally, plants are considered crucial since they are the major source of energy for humanity and have nutritional, therapeutic, and other benefits. Image identification has become more prominent in this technology-driven world, where many innovations are happening in this sphere. Image processing techniques are increasingly being used by researchers to identify plants. The capacity of Convolutional Neural Networks (CNN) to transfer weights learned with huge standard datasets to tasks with smaller collections or more particular data has improved over time. Several applications are made for image identification using deep learning, and Machine Learning (ML) algorithms. Plant image identification is a prominent part of such. The plant image dataset of about 300 images collected by mobile phone and camera from different places in the natural scenes with nine species of different plants are deployed for training. A five-layered convolution neural network (CNN) is applied for large-scale plant classification in a natural environment. The proposed work claims a higher accuracy in plant identification based on experimental data. The model achieves the utmost recognition rate of 96% NU108 dataset and UAV images of NU101 have achieved an accuracy of 97.8%.

Voids identification in rubberized mortar digital images using K-Means and Watershed algorithms

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain.