SETR-YOLOv5n: A Lightweight Low-Light Lane Curvature Detection Method Based on Fractional-Order Fusion Model

Liu, Yang; Wang, Yongfu; Li, Yang; Li, Qiansheng; Wang, Jian

doi:10.1109/access.2022.3203443

Cited by 8 publications

(1 citation statement)

References 44 publications

(41 reference statements)

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“…For example, Liao et al [27] built the device components using a simple online and real-time tracking and counting algorithm on pruned YOLOv5. Liu et al [28] improved YOLOv5n by optimizing the configuration of the target detector head and the network structure, solving the problems of low efficiency and redundant parameters in feature extraction in the model. Additionally, Xu et al [29] proposed a target detection algorithm based on the YOLOv5 algorithm to address the issues of low accuracy and strong interference in existing safety helmet wearing detection algorithms and successfully improves the detection accuracy of safety helmets.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Intelligent Detection System for Illegal Wearing of On-Site Power Construction Worker Based on Edge-YOLO and Low-Cost Edge Devices

Chang

Dang

et al. 2023

Applied Sciences

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Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations. To overcome these challenges, we propose an intelligent detection system that can accurately identify instances of illegal wearing of power construction workers in real-time. Firstly, we integrated the squeeze-and-excitation (SE) module into our convolutional neural network to enhance detection accuracy. This module effectively prioritizes informative features while suppressing less relevant ones, resulting in improved overall performance. Secondly, we present an embedded real-time detection system that utilizes Jetson Xavier NX and Edge-YOLO. This system promptly detects and alerts power construction workers of instances of illegal wearing behavior. To ensure a lightweight implementation, we design appropriate detection heads based on target size and distribution, reducing model parameters while enhancing detection speed and minimizing accuracy loss. Additionally, we employed data augmentation to enhance the system’s robustness. Our experimental results demonstrate that our improved Edge-YOLO model achieves high detection precision and recall rates of 0.964 and 0.966, respectively, with a frame rate of 35.36 frames per second when deployed on Jetson Xavier NX. Therefore, Edge-YOLO proves to be an ideal choice for intelligent real-time detection systems, providing superior accuracy and speed performance compared to the original YOLOv5s model and other models in the YOLO series for safety monitoring at construction sites.

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Section: Related Workmentioning

confidence: 99%