The railway is responsible for the transport of large quantities of goods and passengers, and the danger is high, if railway staff do not wear safety equipment correctly, the probability of accidents will be significantly increased, so it is necessary to improve the detection accuracy of railway safety equipment. In order to prevent safety accidents caused by the lack of railway safety equipment in railway operations and improve the intelligent detection ability of workers' safety helmets and safety vests, this paper proposes an RST-YOLOv8 model based on YOLOv8 model, which replaces the RepViT module in the backbone network to improve the detection accuracy of helmet and vests. SSFF module and TFE module are used in the neck network to improve the detection effect of small targets. Experimental results show that compared with the original YOLOv8 model, the improved model increases mAP50 and mAP50-95 by 1.9 % and 1.7 %, respectively.
Key words
safety equipment testing /
convolutional neural networks /
YOLOv8 /
railway safety
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