Computer Vision enabled Adaptive Speed Limit Control for Vehicle Safety

Lad, Abhi; Kanaujia, Prithviraj; Soumya,; Solanki, Yash

doi:10.1109/aimv53313.2021.9670944

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The use of Convolutional Neural Networks (CNNs) significantly advanced the field of CV in 2012 when CNNS demonstrated high performance in the ImageNet Large Scale Visual Recognition Competition [9], emerging as the predominant learning method in CV. CNNs showed expert-level performance in image classification, object detection, and semantic segmentation across diverse fields, including medicine, surveillance, and autonomous driving [10][11][12][13][14][15][16]. As machine learning models advanced, obtaining a sufficient quantity of labeled data became a challenge.…”

Section: Overview Of Computer Visionmentioning

confidence: 99%

“…Examples of job enhancement include the use of CV to analyze sporting events to inform referee calls [33], the scoring of diving competitions [34], and insurance appraisals to assist with claim reporting [10]. The use of CV in surveillance included the detection of forgery in artwork [35][36][37] as well as other industries, the prevention of cheating in academic and educational settings, and the enforcement of speed limits [14]. The theme of monitoring was the largest, including the use of CV to monitor agricultural crops for disease or insect infestations [15,38,39], detect restocking needs in warehouses or retail stores [40], and identify defective products on assembly lines [41].…”

Section: Application Of Computer Vision In Industry Outside Of Health...mentioning

confidence: 99%

“…Video enhancement [53] Image/video deblurring [54] Education Cheating prevention (S) [55] Engineering Importing real life objects into modeling software [56] Food service Reduce food waste in restaurants (M) [57] Robotic food delivery https://starship.xyz/, accessed on 15 July 2023 Gaming Xbox Kinect [58] Gesture based gaming [59] Government Control traffic lights [60] Detecting natural disasters (M) [61] Insurance Insurance appraisals [10,62,63] Law enforcement Forensic analysis [64] Facial recognition in large crowds (S) [65] Identity verification (S) [66] Detect dangerous situations (S) [67] Speeding enforcement [14] Manufacturing Defective products on an assembly line [68] Manufacturing Workplace inspection [69] Medical See Table 3 Military Terrain Reconnaissance (S) [70] Automate military drones [71] Movie Film movie restoration […”

Section: Digital Designmentioning

confidence: 99%

See 2 more Smart Citations

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

J. Imaging

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Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

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Section: Overview Of Computer Visionmentioning

confidence: 99%

Section: Application Of Computer Vision In Industry Outside Of Health...mentioning

confidence: 99%

Section: Digital Designmentioning

confidence: 99%

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Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

J. Imaging

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show abstract

“…Their work demonstrates the application of deep learning for real-time traffic management, which is relevant for improving navigation decisions in autonomous emergency response systems. Lad et al [5] implemented computer vision techniques for adaptive speed limit control to enhance vehicle safety. This research is pertinent to our work as it highlights the use of CNNs for real-time object detection and decision-making, critical for autonomous navigation.…”

Section: Related Workmentioning

confidence: 99%

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Needhi

2024

IJSREM

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Abstract—This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learn- ing, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services. Index Terms—Object Detection, Convolutional Neural Net- works (CNN), Reinforcement Learning, Q-Learning, AI-Driven Ambulance, Autonomous Systems, Emergency Medical Services, Real-Time Navigation.

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“…For example, in the autonomous areas, accurate and timely target detection can make autonomous driving technology a reality [10][11][12][13]. From the perspective of traffic safety, pedestrian detection can be used to avoid traffic accidents caused by pedestrian intrusion [14][15][16]. In the aspect of safety protection, pedestrian detection and face recognition can control the entry and exit of suspicious persons [17].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Pedestrian Intrusion Detection and Warning Method for Intelligent Traffic Security

2022

KSII TIIS

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As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

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Computer Vision enabled Adaptive Speed Limit Control for Vehicle Safety

Cited by 10 publications

References 18 publications

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

A Lightweight Pedestrian Intrusion Detection and Warning Method for Intelligent Traffic Security

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