A lightweight algorithm based on YOLOv5 for relative position detection of hydraulic support at coal mining faces

Pan, Lihu; Duan, Yuxuan; Zhang, Yingjun; Bin-hong, Xie; Zhang, Rui

doi:10.1007/s11554-023-01292-w

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Ding et al [15] proposed a deep learning training method that fuses infrared and visible samples to improve the performance of the recognition network by expanding the number of samples and features, which effectively improves the problem of low accuracy and poor real-time performance caused by noise, complex background, and occlusion in coal gangue classification detection. Pan et al [16] proposed a machine vision-based detection method for the relative positioning of hydraulic brackets in coal mining driving faces, which effectively reduces the model size and computational effort to meet the requirements of real-time detection. To address the limitations of existing algorithms in extracting image features and identifying targets of different sizes, Shan et al [17] proposed a cascade network incorporating the borehole feature extraction transform (BFET) and borehole detection (BD), which improved the contrast of images and the accuracy of real-time detection.…”

Section: Introductionmentioning

confidence: 99%

Research on the Detection Method of Coal Mine Roadway Bolt Mesh Based on Improved YOLOv7

Sun

Wang

et al. 2023

Electronics

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Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.

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Section: Introductionmentioning

confidence: 99%