Study on safety monitoring technology for chain fracture in crushing station of open-pit mine

doi:10.16265/j.cnki.issn1003-3033.2023.S2.0035

Abstract

Abstract:

The detection efficiency of the chain fracture of the conveying mechanism in the crushing station of the open-pit mine is low, and the processing efficiency of the traditional image recognition method is low, with poor real-time detection performance. To address these issues, a chain fracture detection technology of the crushing station based on deep learning technology was put forward. Firstly, the overall design scheme of the chain fracture detection system was proposed in terms of the hardware platform construction and software architecture. Then, the YOLOv4 model was used to build the chain detection model architecture, and the chain fracture detection algorithm was developed according to the operating conditions of the crushing station. The image preprocessing method was proposed according to the actual environmental characteristics. Then, image samples were collected for iterative training of the model, and the chain target detection and recognition model was obtained. Finally, the chain fracture loss was detected. The results show that the chain target detection of the crushing station based on deep learning can accurately identify the number of chains in the image during the operation of the conveying mechanism, and when the chain is occluded for simulated loss, the model can send a warning in time.

Key words: crushing station of open-pit mine, fracture detection, deep learning, machine vision, YOLO

CLC Number:

X924.3安全监控系统

WEI Dezhi, DU Zhiyong, TENG Chunyang, XIN Wutian, LI Zekun. Study on safety monitoring technology for chain fracture in crushing station of open-pit mine[J]. China Safety Science Journal, 2023, 33(S2): 170-175.

Figures/Tables 10

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检测目标	文中方法识别精度	ALexNet 识别精度	文中方法识别时间/s	ALexNet 识别时间/s
平环	0.972 1	0.987 5	0.046	6.22
立环	0.983 6	0.992 5	0.051	5.89