An Efficient Damage Relief System based on Image Processing and Deep Learning Techniques

The edible potato comes in fifth for human consumption and fourth among main food crops. Since it is a crop that is vegetative grown, many pests and disease can be passed along from one generation to the next. Crop diseases, which have a negative impact on food security as well as economic losses, have a significant impact on the production and quality of yields from potato crops. Thus, the application of unique and precise deep learning-based algorithms for disease detection and classification is highly required. Identifying weaknesses in agricultural products, particularly potatoes requires the use of machine vision and image processing techniques. Deep learning and image processing have been used in agriculture to classify and number of disease and pests affecting potatoes has grown, and study in this area is still continuing. The use of artificial intelligence and image processing in agriculture for the classification and identification of potato pests and disease has grown, and work in this area is still ongoing. Different deep learning techniques, such as VGG19, VGG16, Google Net, Alex Net, and convolution neural network methods, can be used to address the disease problem in potatoes. These methods also examined multiple classes of potato diseases as: Healthy, Black Leg, Black Scurf, Pink Rot, Common Scab, etc. Food safety could be seriously threatened by the spread of potato disease. In this article, deep learning techniques for early detection of potato disease are discussed.

Quantification of legumes biological nitrogen fixation (BNF) is normally done via analytical methods that require sampling, drying, grinding, and laboratory processing. These methods are time-consuming, expensive, and are not accessible to growers. The correlation between the BNF quantity and nodule number and nodule mass can be used to develop tools that allow rapid assessments of the BNF. In this work, we developed a graphical user interface (GUI) based deep learning and image processing system for legume nodule segmentation and classification to determine the characteristics associated with legume nodules using digital images. During image acquisition, the legume root samples were imaged using a smartphone camera and lab-made imaging setup. A total of 1468 digital images were collected from 367 root systems. After the first run of imaging, nodules were separated from the roots, and another image was obtained from the nodules of each sample. For comparison and validations, nodules were manually counted, dried, and weighed. In this study, a categorized image data library was developed and utilized for deep learning and image processing. Digital image processing filters, and image segmentation method were applied to process the digital images of the root systems and determine the number of nodules and provide their characteristics. Deep learning models were used to classify the images into different legume classes. Furthermore, a GUI was successfully developed to simplify the utilization and application of deep learning/digital image processing algorithms. The preliminary results of this study demonstrate that our deep learning/image analysis system has a great potential to accurately quantify, characterize, and count the nodules and that could be extremely valuable to growers.

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Deep Learning is a significant tool that communicates with the computer to perform task as a natural human being. Deep learning algorithms exhibits a vital role in defining the features and can assist the large number of processes for the data which are structured or unstructured. In recent years, this concept is receiving quite a lot attention for its exceeding achievements in human-level performance. In deep learning, systematic and rapid development of diverse lifestyle, including marketing, policing, finance, manufacturing and education modeling which creates pioneering technologies such as autonomous systems. Deep learning being a subset of machine learning is tackling problems with advanced features. Deep Learning is basically a neural network with processing units consisting of multiple layers with which it has efficaciously applicable to a wide set of problems in the areas such as image processing, image recognition and natural language processing. This paper gives a brief review on the different approaches, algorithms used in deep learning techniques from the beginning to the present scenario. The main aim of this study is to reveal the advantages features of the handsome approaches utilized in the deep learning process. Deep learning methods are contributing a diversity of assistances not only to online learners but also to institutions that advance in current eLearning platforms. This survey will afford an overview of the key ideas on the concept, approaches, advantages and impact of deep learning in the area of research.

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay; 2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability; 3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system.

Unmanned aerial vehicles (UAVs) play an essential role in emergency cases and adverse environments for applications like disaster detection and mine exploration. To process the massive volume of sensing data generated by various sensory payloads in these applications, existing works either compress deep learning (DL) models to conduct onboard computing, or offload raw data back to the resourceful ground station with the help of relay UAVs due to base station damage. However, the former sacrifices the inference accuracy of DL models (up to 10% accuracy loss), while the latter achieves high accuracy at the cost of significant latency, due to limited wireless communication resources in the multi-hop transmission. To address the problem, exploiting the resources of the UAV swarm including both task UAVs and relay UAVs, we build up an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>ll-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>ky <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>utonomous com<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u>uting (ASAP) system to autonomously conduct collaborative computing in the swarm, to achieve both high accuracy and low latency of sensing data processing. In detail, we first propose a novel UAV swarm-native collaborative computing architecture, considering the general hierarchy and clustering structure of UAV swarms, as well as the characteristic of DL model execution. We then design an elastic efficient task scheduler to allocate computing tasks for UAVs, and update the scheduling scheme online when some UAVs are unavailable, with the aid of a lightweight and accurate DL inference performance predictor. Finally, we design an adaptive inter-UAV data compressor, to adapt to the limited and dynamic communication resources between UAVs. Experiment results on 24 airborne computers and five real-world UAVs show that, the proposed system can perform collaborative computing in a timely manner and effectively deal with situations when some UAVs become unavailable.

&lt;span lang="EN-US"&gt;A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.&lt;/span&gt;

In this paper, we present a set of studies of detecting facemask to prevent COVID-19 spread using machine learning, deep learning, and artificial intelligence. It also explains some recent studies based on the hybrid concepts of deep learning and image processing techniques in this domain. This paper is mainly focused on the set of studies that analyze the machine learning capabilities to detect facemasks through the scanning of people’s images. Besides that, this paper also describes some popular techniques to analyze and detect the safe distance among people using image processing with machine learning techniques. However, it describes a brief comparison among these recent popular studies in terms of technology used, model testing and usage, and accuracy prediction. Readers can find some useful references at the end of this paper.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs' trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

Corrosion causes irreversible damage to metal structures and is one of the most common causes of bridge failure. The advent of affordable unmanned aerial vehicles (UAVs) that can carry digital cameras close to a structure presents an opportunity to quickly inspect bridges for corrosion. However, imagery-based corrosion assessment techniques must be accurate, detailed, and comprehensive to be effectively used in bridge inspections. This paper reviews and evaluates the use of imagery data and machine learning for corrosion assessment on bridges, analyzing existing research on imagery-based corrosion assessment, examining the ongoing difficulties of proposed data analysis approaches, and determining the remaining gaps in the literature. This paper evaluates the existing work with an emphasis on image processing methods; the environment and conditions in which the methods have been tested; evaluation of metrics used for performance assessment; and distinctions between corrosion detection, localization, and segmentation, as are significant in supporting bridge inspection decisions. The analysis finds that image processing techniques are typically too prone to false positives to perform well on chaotic data, particularly with nonuniform lighting and misleading objects in the image, which would be collected during bridge inspections using UAVs. Deep learning algorithms have been reported to perform more accurately than the human benchmark on similar data, and multiple researchers have developed deep learning algorithms that work well on UAV-collected data. However, there remain gaps in performance between methods tested on laboratory data compared to field data and on human-collected compared to UAV-collected data. Algorithms need to be trained on larger, more variable data sets to demonstrate generalizability across bridge inspection applications. If addressed, these methods could improve the safety, time-, and cost-efficiency of bridge inspections; help inspectors understand the state of each asset; and support structural repair, reconstruction, and prioritization decisions before failures occur.

Accurate eye detection in thermal images is essential for diverse applications, including biometrics, healthcare, driver monitoring, and human-computer interaction. However, achieving this accuracy is often hindered by the inherent limitations of thermal data, such as low resolution and poor contrast. This work addresses these challenges by proposing a novel, multifaceted approach that combines both deep learning and image processing techniques. We first introduce a unique dataset of thermal facial images captured with meticulous eye location annotations. To improve image clarity, we employ Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, we explore the effectiveness of advanced YOLO models (YOLOv8 and YOLOv9) for accurate eye detection. Our experiments reveal that YOLOv8 with CLAHE-enhanced images achieved the highest accuracy (precision and recall of 1, mAP50 of 0.995, and mAP50-95 of 0.801), the YOLOv9 model also demonstrated excellent performance with a precision of 0.998, recall of 0.998, mAP-50 of 0.995, and mAP50-95 of 0.753. Furthermore, to enhance the resolution of detected eye regions, we investigate various super-resolution techniques, ranging from traditional methods like Bicubic interpolation to cutting-edge approaches like generative adversarial networks (BSRGAN, ESRGAN) and advanced models like Real-ESRGAN, SwinIR, and SwinIR-Large with ResShift. The performance of these techniques is evaluated using both objective and subjective quality measures. Overall, this work demonstrates the effectiveness of our proposed pipeline, which seamlessly integrates image enhancement, deep learning, and super-resolution techniques. This synergic fusion significantly improves the contrast, accuracy of eye detection, and overall resolution of thermal images, paving the way for potential applications across various fields.

Nowadays, image processing solution is used in many fields such as traffic information systems and illegal intrusion detection systems. Now, to assist with the control of camera-equipped devices, appropriate image processing techniques are needed for moving rather than fixed observers. For achieving this goal, an algorithm should derive the desired results quickly and accurately; thus, this paper considers two characteristics: functional performance (reliability) and temporal performance (efficiency). Reliability means how well the desired results can be achieved, and efficiency means how quickly the result can be calculated. This paper suggests an optimized real-time image algorithm based on the integration of the optical flow and Speeded-Up Robust Features (SURF) algorithms. This algorithm determines horizontal or vertical movement of the camera and then extracts its displacement. The proposed algorithm can be used to stabilize an Unmanned Aerial Vehicle (UAV) in situations where it is drifting due to inertia and external forces, like wind, in parallel. The proposed algorithm is efficient in achieving drift stabilization by movement detection; however, it is not appropriate for image processing in small UAVs. To solve this problem, this study proposes an image processing method that uses a high-performance computer.

To develop a robust and effective design, the proposed research uses a model that analyses blood vessels in retinal images by leveraging Deep Learning and Image Processing techniques. Deep learning, one of the AI techniques, has experienced rapid growth and is widely used in the medical field to analyse medical images, driving advancements in technology and medicine. The U-Net semantic segmentation neural network is the primary deep learning model used in our study. Training data consists of a set of retinal images, and the model’s performance is assessed on the training set. Once testing is complete, the results help evaluate the accuracy and effectiveness of the model and the techniques utilised in this research. This study indicates that deep learning segmentation techniques can offer valuable insights beyond standard pixel-based detection techniques. The proposed method gives a better understanding of retinal blood vessel analysis. Researchers used the DRIVE dataset for this research. It has 20,512x512 training and testing images for the model’s training and testing. The neural network's blood vessel shape recognition helps diagnose eye diseases. Diseases include hypertension, diabetic retinopathy, etc. The neural network's effectiveness makes the system suited for real-time blood vessel detection. This paper accurately mapped major and minor blood arteries. Thus, 96.53% of this research succeeded. The mean intersection over union is 8184. This exceeds the previous E-NET model of 7744.

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).

The development and structuring of an augmented reality (AR) platform integrating image processing, deep learning, and spatial tracking techniques have yielded promising results. AR technology, which enhances real-world objects with computer-generated information, holds vast potential for education, entertainment, and industry. Image processing enables real-time object detection, recognition, and tracking within the user's environment, while deep learning models enhance pattern recognition accuracy. Spatial tracking techniques ensure seamless integration of virtual content with the physical world, providing immersive AR experiences. Ant colony optimization (ACO) algorithms further enhance the AR platform's functionality by optimizing object placement and interaction. ACO mimics ant behavior to find optimal paths through graphs, effectively optimizing spatial layout and user interaction patterns in dynamic environments. The integration of ACO into the AR platform facilitates efficient pathfinding for augmented objects and enhances the system's responsiveness to user inputs. The successful implementation of the AR platform underscores its potential to revolutionize various industries, including healthcare, education, and marketing. By leveraging AI-driven algorithms and optimization techniques, AR systems can deliver personalized and engaging experiences that blur the boundaries between the digital and physical worlds. These results pave the way for future advancements in AR technology, driving innovation and reshaping human-computer interaction paradigms.