Survey on AI‐Enabled Computer Vision Technologies and Applications for Space Robotic Missions

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. Data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.KeywordsDeep neural network algorithmComputer visionPattern recognitionTechnical application

Advanced Driver Assistance Systems (ADAS) have emerged as a critical technology in enhancing vehicle safety, providing convenience to drivers, and facilitating the transition toward fully autonomous vehicles. The core of these systems relies heavily on computer vision (CV) technologies, which enable vehicles to interpret and understand their surrounding environment in real time. The integration of computer vision in ADAS allows vehicles to recognize and process various objects such as pedestrians, vehicles, traffic signs, and lane markings, significantly improving both safety and the driving experience. In this paper, we explore the pivotal role of computer vision in the development and implementation of ADAS in autonomous vehicles. We begin by reviewing the evolution of ADAS, detailing the different sub-systems such as lane departure warning, automatic emergency braking, adaptive cruise control, and parking assistance, all of which depend on sophisticated image processing techniques. Furthermore, the paper examines the intersection of computer vision with other emerging technologies such as machine learning, deep learning, and sensor fusion. We discuss how these innovations enable ADAS to function more effectively in complex, dynamic environments. The methodology section focuses on cutting-edge algorithms used for object detection, semantic segmentation, and real-time image processing, which are integral to the functionality of modern ADAS. Additionally, we evaluate the challenges that computer vision systems face in real-world applications, including issues with sensor calibration, environmental conditions, computational limitations, and the integration of data from multiple sensors (e.g., cameras, LiDAR, radar). The paper also presents an analysis of the results obtained from various real-world implementations of ADAS technologies, highlighting improvements in driver safety and the reduction of traffic-related accidents. We explore the potential of computer vision in enabling fully autonomous driving by addressing the current gaps in technology and the advancements needed to close these gaps. Finally, the paper concludes with a discussion on the future of ADAS and the role of computer vision in realizing fully autonomous, reliable, and efficient transportation systems. We argue that continued research and development in computer vision, combined with innovations in deep learning and sensor technologies, are critical to advancing the capabilities and reliability of autonomous vehicles in the near future.

Semantic segmentation is a core technology in computer vision. It plays a pivotal role in various real-world applications, including street scene recognition and medical image analysis. Current approaches of semantic segmentation predominantly leverage deep learning techniques and employ pixel-level decision making. However, these methods often introduce distortions in edge regions, which compromises segmentation accuracy. To address this issue, this study presents an efficient network architecture designed to extract edge features from images and incorporate them as the auxiliary input. This is further integrated with state-of-the-art models for joint training. Experimental results show significant improvements in accuracy and edge handling for semantic segmentation.

A computer vision and robotic system for an intelligent workstation

Recent developments in computer vision (CV) technology have significantly improved the management of beef cattle feeding systems by enabling precise monitoring and adjustment of feed intake based on individual needs. This study introduces an automated approach for identifying cattle at feeding bunks using CV and evaluates the effectiveness of a feeding bunk scoring system to optimize cattle feeding strategies. Utilizing a high-definition video capture setup, our research focused on the feeding behaviors of 6 heifers in one pen with the Calan Feeding System (American Calan, Northwood, NH). We deployed three Reolink PoE cameras (Model D400) strategically positioned above the feeding bunks, each overseeing two feeding bunks, to monitor feeding activity comprehensively. The cameras were set to record in Full HD (1920 x 1080 pixels) at 30 frames per second. We processed the frames using Python’s OpenCV, resulting in 600 images that composed an array with dimensions 600 x 400x450 (pixels) x 3 (RGB). These images served as the basis for our analysis, which involved feature extraction via a ResNet-50 convolutional neural network, followed by dimensionality reduction through Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Our methodology included the use of k-means clustering to detect the presence of cattle at the bunk, achieving an Adjusted Rand Index (ARI) of 0.9933. We applied the DBSCAN clustering algorithm in the ‘cattle present’ images for individual animal identification, obtaining an ARI of 0.9174, indicating high model accuracy. To assess feeding efficiency, we analyzed 12,156 images of feeding bunks obtained using the same Python’s OpenCV approach and then classified them into six categories based on Lundy et al., 2015: S00 (no feed), S05 (scattered feed), S10 (thin layer), S20 (25 to 50%), S30 (>50%), and S40 (untouched). Using a training, validation, and testing split of 70%, 15%, and 15%, respectively, our model demonstrated exceptional precision, recall, and F1 scores of 0.9989 and predictions in the Confusion Matrix testing accuracy of 99.89%, showcasing the model accuracy. However, when applied to a similar classification using the same Calan gate system, with 7,905 images, the performance of the model decreased, with less precision (0.5868), recall (0.3982), and F1 score (0.3653), most related to classes S10, S20, and S40, underscoring the need for further model refinement. Our findings highlight the potential of integrating CV into precision livestock farming, automating the identification of cattle at feeding bunks, and correlating it with feeding scores to estimate individual consumption accurately. This integration promises to enhance sustainable farming by enabling more precise resource utilization and optimizing individual animal performance. With CV technology, producers can significantly improve production efficiency, health management, and environmental sustainability within their operations. The technology also has a crucial role in reducing operational costs, making it a cost-effective and sustainable production method.

Recent advances in the use of computer vision technology in the quality assessment of fresh meats

Using “off-the-shelf” tools for terabyte-scale waveform recording in intensive care unit: Computer system design, database description, and lessons learned

The advances in the development and application of computer hardware, software, and electronic technologies and control systems provides strong support to fast, consistent measurements, data collection, and information analysis for solving problems in the drying industry. One of the most usable and prominent methods and technology used in the drying industry is computer vision technology. Modern real-time computer vision systems (CVSs) can be the best alternative for ill-defined processes monitoring, automating, and controlling due to its rapidness, cheapness, nondestructiveness, sensitiveness, and preciseness. The chapter reviews the current status of CVS use in fruit drying technology, with particular reference to apple and kiwifruit drying processes, and highlights the importance of applying intelligent control systems to solve food quality and energy hindrances. For extraction of quality parameters, the CVS monitoring process must be done in five steps: image acquisition, image processing, feature extraction, pattern recognition, and finally decision making.

Melting characteristics of cheese: analysis of effects of cooking conditions using computer vision technology

With the faster growing technology, Computer Science emerging the different new technologies in different fields. Out of all fields, Artificial Intelligence is one of the newer technologies which have emerging features like machine learning, Internet of Things-IoT, Robotics, etc. Here we have to mention chapter 1includes different trends in Artificial Intelligence, its types, How it works, Challenges in Artificial Intelligence, AI Based Cybersecurity, AI Analytics, Computer Vision Technology, Natural Language processing(NLP), AI for Code Generation, Predictive Analytics, its benefits, models, tools and techniques, Prescriptive Analytics, How Prescriptive Analytics works, advantages and disadvantages of Prescriptive Analytics .

The field of computer vision is now a multi-billion dollar enterprise, with its use in surveillance applications driving this large market share. In the last six years, computer vision researchers have started to discuss the risks and harms of some of these systems, mostly using the lens of fairness introduced in the machine learning literature to perform this analysis. While this lens is useful to uncover and mitigate a narrow segment of the harms that can be enacted through computer vision systems, it is only one of the toolkits that researchers have available to uncover and mitigate the harms of the systems they build. In this monograph, we discuss a wide range of risks and harms that can be enacted through the development and deployment of computer vision systems. We also discuss some existing technical approaches to mitigating these harms, as well as the shortcomings of these mitigation strategies. Then, we introduce computer vision researchers to harm mitigation strategies proposed by journalists, human rights activists, individuals harmed by computer vision systems, and researchers in disciplines ranging from sociology to physics. We conclude the monograph by listing principles that researchers can follow to build what we call community-rooted computer vision tools in the public interest, and give examples of such research directions. We hope that this monograph can serve as a starting point for researchers exploring the harms of current computer vision systems and attempting to steer the field into community-rooted work.

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which has low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unmanned Aerial Vehicle (UAV) can effortlessly access difficult places during any disaster and collect high resolution imagery that is required for aforementioned tasks of computer vision. To address these issues we present a high resolution UAV imagery, FloodNet, captured after the hurricane Harvey. This dataset demonstrates the post flooded damages of the affected areas. The images are labeled pixel-wise for semantic segmentation task and questions are produced for the task of visual question answering. FloodNet poses several challenges including detection of flooded roads and buildings and distinguishing between natural water and flooded water. With the advancement of deep learning algorithms, we can analyze the impact of any disaster which can make a precise understanding of the affected areas. In this paper, we compare and contrast the performances of baseline methods for image classification, semantic segmentation, and visual question answering on our dataset. FloodNet dataset can be downloaded from here: https://github.com/BinaLab/FloodNet-Supervised_v1.0.

Enablers to computer vision technology for sustainable E-waste management

The unsafe behavior of construction workers is one of the main causes of safety accidents at construction sites. To reduce the incidence of construction accidents and improve the safety performance of construction projects, there is a need to identify risky factors by monitoring the behavior of construction workers. Computer vision (CV) technology, which is a powerful and automated tool used for extracting images and video information from construction sites, has been recognized and adopted as an effective construction site monitoring technology for the identification of risky factors resulting from the unsafe behavior of construction workers. In this article, we introduce the research background of this field and conduct a systematic statistical analysis of the relevant literature in this field through the bibliometric analysis method. Thereafter, we adopt a content-based analysis method to depict the historical explorations in the field. On this basis, the limitations and challenges in this field are identified, and future research directions are proposed. It is found that CV technology can effectively monitor the unsafe behaviors of construction workers. The research findings can enhance people’s understanding of construction safety management.

The computer vision direction in the field of artificial intelligence analyses the latest progress of computer vision technology from visual perception and visual generation, including but not limited to image recognition, target detection and image segmentation. First of all, for computer vision technology, this paper introduces the detailed application of image recognition technology, object detection technology and image segmentation technology. Then, we build a BP neural network combined with a deep LSTM neural network, use the BP network algorithm to select the input variables to reduce the dimension and complexity of the model, and use the selected variables as the input of the deep LSTM network. At the same time, deep LSTM is used to perform high-dimensional deep memory learning features on the selected variables. Finally, the model is separately experimented in computer vision. The experimental results show that the present model and other single models can be selected by BP neural network variables in computer vision applications, which can effectively reduce the complexity of the model and improve the generalisation ability of the model, so that it can be used in computer vision research.