Temperature detection algorithm of roller based on improved YOLOv7

doi:10.16265/j.cnki.issn1003-3033.2023.S2.0001

Abstract

Abstract:

In order to ensure the steady operation of the coal transportation line and realize the intelligent monitoring and fault early warning of the roller, the intelligent inspection robot equipped with an infrared camera was used to perform fault inspection. The inspection robot adopted fixed-point shooting to collect infrared images in real time and used the target recognition algorithm to determine the characteristics of the roller and locate the roller. The inspection robot collected the temperature measurement information during roller operation, and the AI target detection algorithm was used to judge the abnormally high temperature of the roller bearing and detect abnormal roller temperature. Based on the detection results of the roller, the extracted temperature value, and the actual mileage value, an alarm signal was issued to the operator to achieve fault early warning. The detection of abnormal roller drew on the idea of the Transformer network and introduced recursive gated convolution (gⁿConv) to improve the YOLOv7 algorithm. The results show that the average precision of the improved YOLOv7 algorithm is up to 0.98 and meets the requirements of real-time processing. The accuracy of the improved YOLOv7 algorithm is increased by 3.1%; the recall rate is increased by 0.4%; the average precision is increased by 0.03%, and the improved YOLOv7 algorithm has a better detection effect.

Key words: belt conveyor, infrared camera, fault early warning, YOLOv7, roller temperature inspection

CLC Number:

X936

ZHANG Lei, JIRI Gele, SHANG Shuhong. Temperature detection algorithm of roller based on improved YOLOv7[J]. China Safety Science Journal, 2023, 33(S2): 222-227.

Figures/Tables 11

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红外托辊	精确率P/%		召回率R/%		mAP@0.5
	YOLOv7	改进 YOLOv7	YOLOv7	改进 YOLOv7	YOLOv7	改进 YOLOv7
	94.2	97.3	93.1	94.5	0.95	0.98