Safety Helmet Wearing Detection Model Based on Improved YOLO-M

Wang, Lili; Zhang, Xinjie; Yang, Hailu

doi:10.1109/access.2023.3257183

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…The inference speed of the model reaches 22.9 FPS. In 2023, Wang et al [8] proposed a helmet-wearing detection model called YOLO-M. The model employed MobileNetv3 as the backbone network, effectively reducing model complexity.…”

Section: Related Work 21 Violation Behavior Detectionmentioning

confidence: 99%

SP-YOLO-Lite: A Lightweight Violation Detection Algorithm Based on SP Attention Mechanism

Huang

Xie

et al. 2023

Electronics

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In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are difficult to deploy on low-computational-power embedded platforms situated at the edge due to their high model complexity. These algorithms suffer from drawbacks such as low operational efficiency, slow detection speed, and high energy consumption. To address this issue, a lightweight violation detection algorithm based on the SP (Segmentation-and-Product) attention mechanism, named SP-YOLO-Lite, is proposed to improve the YOLOv5s detection algorithm and achieve low-cost deployment and efficient operation of object detection algorithms on low-computational-power monitoring platforms. First, to address the issue of excessive complexity in backbone networks built with conventional convolutional modules, a Lightweight Convolutional Block was employed to construct the backbone network, significantly reducing computational and parameter costs while maintaining high detection model accuracy. Second, in response to the problem of existing attention mechanisms overlooking spatial local information, we introduced an image segmentation operation and proposed a novel attention mechanism called Segmentation-and-Product (SP) attention. It enables the model to effectively capture local informative features of the image, thereby enhancing model accuracy. Furthermore, a Neck network that is both lightweight and feature-rich is proposed by introducing Depthwise Separable Convolution and Segmentation-and-Product attention module to Path Aggregation Network, thus addressing the issue of high computation and parameter volume in the Neck network of YOLOv5s. Experimental results show that compared with the baseline network YOLOv5s, the proposed SP-YOLO-Lite model reduces the computation and parameter volume by approximately 70%, achieving similar detection accuracy on both the VOC dataset and our self-built SMPC dataset.

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Section: Related Work 21 Violation Behavior Detectionmentioning

confidence: 99%

SP-YOLO-Lite: A Lightweight Violation Detection Algorithm Based on SP Attention Mechanism

Huang

Xie

et al. 2023

Electronics

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“…In Ref. [10], an improved YOLOv5 algorithm (called YOLO-M) was proposed to detect whether a worker is wearing a helmet in an occluded scene. Chen et al added module into the backbone network.…”

Section: Introductionmentioning

confidence: 99%

Insulating Glove Wearing State Detection for Substation Personnel Based on Faster‐YOLOv8

Kang,

Huang,

Huang

et al. 2024

IEEJ Transactions Elec Engng

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Electrical workers must wear insulating gloves during daily maintenance. Detecting the condition of these gloves is significant for power safety. Due to the limitations of existing detection algorithms in accuracy and lightweight on power construction sites, this paper proposes a Faster‐YOLOv8 algorithm to detect the wearing condition of insulating gloves. First, it replaces the Bottleneck in the C2f module with the Faster Block and introduces a new C2Faster module. This reduces model parameters while improving performance. Second, the feature pyramid is improved to enhance the combination of deep and shallow semantic information. This improvement aims to improve the network's focus on detecting small targets. Finally, the GSConv is introduced into the neck network to reduce the model parameters and calculation. Additionally, GSConv enhances feature information exchange through shuffle operation. The effectiveness of the Faster‐YOLOv8 algorithm is verified through comparisons with mainstream algorithms such as the Faster‐RCNN algorithm, SSD algorithm, YOLOv5 algorithm, and YOLOv7‐tiny algorithm. The results demonstrate that the Faster‐YOLOv8 algorithm outperforms the above algorithms in terms of detection accuracy. Compared with the original YOLOv8 network model, the mAP is improved by 3.8%, the mAP of the wrong glove increased by 9.8%, the model size is reduced by 33.3%, and the detection speed can reach 81 FPS. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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