SAD: Sensor-Based Anomaly Detection System for Smart Junctions

Krishnendhu, S P; Mohandas, Prabu

doi:10.1109/jsen.2023.3297205

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…EfficientDet stands as another outstanding one-stage object detection model, integrating EfficientNet as its backbone network and employing composite scaling strategies to achieve efficient and high-precision object detection [36]. DETR (Data-efficient Object Transformer) is a Transformer-based object detection model that successfully introduced self-attention mechanisms to handle contextual information in images [37]. Additionally, Deformable DETR builds upon DETR by introducing deformable attention mechanisms to further enhance object detection performance, especially in scenarios involving multiple overlapping or complex objects [38].…”

Section: Research On One-stage Approaches In Object Detectionmentioning

confidence: 99%

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. However, current intelligent traffic systems still face various challenges, particularly in the realm of target detection. These challenges include adapting to complex traffic scenarios and the lack of precise detection for multiple objects. To address these issues, we propose an innovative approach known as YOLOv8-SnakeVision. This method introduces Dynamic Snake Convolution, Context Aggregation Attention Mechanisms, and the Wise-IoU strategy within the YOLOv8 framework to enhance target detection performance. Dynamic Snake Convolution assists in accurately capturing complex object shapes and features, especially in cases of target occlusion or overlap. The Context Aggregation Attention Mechanisms allow the model to better focus on critical image regions and effectively integrate information, thus improving its ability to recognize obscured targets, small objects, and complex patterns. The Wise-IoU strategy combines dynamic non-monotonic focusing mechanisms, aiming to more precisely regress target bounding boxes, particularly for low-quality examples. We validate our approach on the BDD100K and NEXET datasets. Experimental results demonstrate that YOLOv8-SnakeVision excels in various complex road traffic scenarios. It not only enhances small object detection but also strengthens the ability to recognize multiple targets. This innovative method provides robust support for the development of intelligent traffic systems and holds the promise of achieving further breakthroughs in future applications.

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Section: Research On One-stage Approaches In Object Detectionmentioning

confidence: 99%

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Liu,

Lin

2023

Electronics

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IoT Sensors for Smart Automation

Chayalakshmi,

Kakkasageri,

Pujar

et al. 2023

AI and Blockchain Applications in Industrial Robotics

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The internet of things (IoT) has changed the comforts of every individual. Sensors and communication models play an important role in the effective implementation of IoT systems for any applications. Sensors can be classified based on the parameters to be measured and on the working principles. The second significant component in IoT-based systems is communication models, which help to exchange the data between the two IoT devices. Basically, there are four types of communication models that are commonly used: device to device, device to cloud, device to the gateway, and backend data sharing communication models. This chapter explains various sensors that can be connected to the internet for transferring data between IoT devices, application areas and communication models. The classification of IoT application areas is based on the scope of functionality, use, and adaptation. Sensors are classified on the basis of types of measurement such as vision, position, physical, and others. Basic communication models and open research challenges are explained at the end.

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A Survey on Comprehensive Exploration of Modern Perception System

Rishika S,

Rakshitha V,

Poornima R M

2024

IJARSCT

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This review paper offers an in-depth analysis of recent advancements in image-based intelligent transportation systems. It provides insights into various approaches employed for detecting false images, license plate recognition, distributing emergency messages, as well as detecting and classifying vehicle accidents and road accidents. The research critically evaluates the pros and cons of deep learning techniques, including position-based routing, enhanced YOLOv8, optimal K-means with a convolutional neural network, and gated hierarchical multi-task learning. The investigation of an Internet of Things-based vehicle accident detection and classification system highlights the benefits of sensor fusion for precise accident detection; nonetheless, the evaluation raises questions regarding the scalability and reliability of smartphone sensors. The utility of a deep learning system for road accident identification utilizing artificially created multi-perspective movies is emphasized, along with warnings about possible limits in capturing real-world circumstances

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SAD: Sensor-Based Anomaly Detection System for Smart Junctions

Cited by 3 publications

References 33 publications

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

IoT Sensors for Smart Automation

A Survey on Comprehensive Exploration of Modern Perception System

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