An Improved Detection Method for Dressing Behavior of Substation Workers Based on SSD

Zhu, Mingzeng; Liu, Xiaolan; Zhou, Haijing; Hu, Kaibo; Huang, Xinhua; Qiu, H.

doi:10.1145/3452940.3453056

Cited by 1 publication

(1 citation statement)

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“…Currently, deep learning methods have been widely employed by researchers for the detection of safety protective equipment in electrical operations [4]. Zhu et al [5] proposed a new model to replace the Vgg-16 network in SSD with the lightweight mobile netv3-small network and perform feature fusion of deep features and shallow features. The improved SSD method achieved a speed of 108FPS for detecting helmets at substation sites.…”

Section: Introductionmentioning

confidence: 99%

Detection Model of Substation Personnel Safety Protection Tools Based on Improved YOLOv5

Peng,

Liao,

Zhong

et al. 2023

J. Phys.: Conf. Ser.

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Substation operation and maintenance personnel face the hazards of high voltage and strong current in their work. Therefore, it is crucial to accurately and promptly detect the condition of safety protection tools for these personnel. This paper proposes a detection model that enhances YOLOv5‘s substation personnel safety protection tool detection capabilities. The CBAM attention mechanism was integrated into the YOLOv5 model using this approach, while the feature fusion network PANet was replaced with the CB-BiFPN structure. Finally, DIoU was used instead of GIoU loss function to improve the convergence speed and detection accuracy of the model. Experimental results demonstrate that our improved YOLOv5 model achieves high accuracy and fast processing speed with a mean average precision (mAP) of 89.23% at 32 frames per second (FPS). Our proposed method outperforms other related object detection models such as Faster RCNN, SSD, and YOLOv5 in terms of performance metrics. This study improves safety protection tool detection accuracy and provides valuable insights for real-time safety management and control in substations.

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Section: Introductionmentioning

confidence: 99%