Research on Infrared Obstacle Detection of Mine Roadway Based on RCR_YOLOv4
Received date: 2022-01-05
Revised date: 2022-05-09
Online published: 2022-10-31
Aiming at the problem that driving obstacles such as falling rocks or pedestrians may appear on the dim road of underground mine roadway，which seriously affects the safe driving of unmanned mine card，a fast detection optimization model RCR_YOLOv4 of roadway obstacles based on infrared vision recognition was proposed.Firstly，the infrared camera was used for data acquisition，random cutting，random flipping，mirror flipping and other methods were used to expand the experimental data set.Labeling software was used for data Labeling，and the infrared obstacle data set of mine roadway was built and loaded into the obstacle detection model.Secondly，K-Means ++ optimization algorithm was used to screen the prior frame size of obstacles in the roadway，and depth separable convolution was introduced to reduce the number of network parameters and computation，so as to improve the positioning accuracy and detection efficiency of obstacle targets.The dual-channel attention mechanism is designed to optimize the network feature fusion module to realize the high-precision detection of the obstacle of unmanned mine jamming.The results show that the detection accuracy of the model can reach 93.52% and the detection speed can reach 60.6 FPS.Compared with the current popular target detection networks such as Faster_RCNN，SSD and YOLOv4，RCR_YOLOv4 also shows better comprehensive performance and can provide guarantee for the safe driving of unmanned mine cards in the complex environment of mine roadway.
Key words： mine roadway; driverless; machine vision; obstacle detection; infrared image; YOLOv4
Shunling RUAN , Lijuan DONG , Caiwu LU , Qinghua GU . Research on Infrared Obstacle Detection of Mine Roadway Based on RCR_YOLOv4[J]. Gold Science and Technology, 2022 , 30(4) : 603 -611 . DOI: 10.11872/j.issn.1005-2518.2022.04.013
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