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Gold Science and Technology >

2023 , Vol. 31 >Issue 6: 953 - 963

DOI: https://doi.org/10.11872/j.issn.1005-2518.2023.06.120

Mining Technology and Mine Management

Road Rockfall Detection in Mining Area Based on Weighted Bidirectional Feature Fusion

  • Qinghua GU , 1, 2 ,
  • Yifan DU , 1 ,
  • Pingfeng LI 3 ,
  • Dan WANG 2
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  • 1. School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, Shaanxi, China
  • 2. Xi’an Key Laboratory of Intelligent Industry Perception Computing and Decision Making, Xi’an 710055, Shaanxi, China
  • 3. Hongda Blasting Engineering Group Ltd. , Co. , Guangzhou 511300, Guangdong, China

Received date: 2023-08-22

  Revised date: 2023-11-28

  Online published: 2024-01-26

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Abstract

With the booming development of big data and Internet of Things technology，traditional mines have developed to smart mines and intelligent mines，and unmanned technology has been gradually applied to mining areas.In order to solve the problem that the rockfall detection of unstructured road in open-pit mine area has complex environment，large difference in rockfall size and similar color between rockfall and unstructured road surface，a rockfall detection model of mining road based on weighted bidirectional feature fusion was proposed.First，the SimAM attention mechanism is added to the backbone network，this attention mechanism is different from the previous channel attention mechanism and spatial attention mechanism，it can effectively eliminate the interference of the background environment without adding additional parameters，so that the model can focus more on the target characteristics of rockfall.Second，the weighted bidirectional feature pyramid（BiFPN）structure was used to realize multi-scale feature fusion in the neck.Since the PANet structure in the YOLOv5s network model only adds or splice the characteristics of the pyramid structure in the melting process，the bidirectional feature weighting was combined with the bidirectional feature of the weight and adaptive adjustment to ensure that the network model attaches proper importance to the rock ebaissees different sizes and different levels and realizes the addition between the low-level position information and high-level semantic information for multiple cross-layer weighted feature fusion，thus enhancing the feature extracion ability of the model for rockfall of different sizes.Finally，the lightweight convolution GSConv module was introduced into the col，which can be used to process function cards at this time，not only reducing redundant information，but also avoiding compression.The GSConv lightweight convolution module is based on deep separable convolution（DSC），ordinary convolution（SC） and channel shuffle operation，which improves the detection speed of the model by effectively reducing the complexity of the model.The experimental results show that the average detection accuracy of this algorithm reaches 92.8%.and the detection speed reaches 63.1FPS.Compared with the current fastest R-CNN，YOLOv4-tiny，YOLOv7 and YOLOv5s algorithms，the average detection accuracy is increased by 17.0，13.6，3.4 and 2.5 percentiles，and the detection speed is increased by 32.2，1.4，14.6 and 2.6 FPS，respectively.Moreover，the model size of the algorithm is only 12.9 MB，which is easy to deploy on mobile devices.Therefore，the algorithm can realize the real-time and accurate detection of unstructured road rockfall in mining area，and ensure the safe driving of unmanned mining card.

Key words： mining area road; unmanned driving; machine vision; rockfall detection; multi-scale feature fusion; YOLOv5 model

Cite this article

Qinghua GU , Yifan DU , Pingfeng LI , Dan WANG . Road Rockfall Detection in Mining Area Based on Weighted Bidirectional Feature Fusion[J]. Gold Science and Technology, 2023 , 31(6) : 953 -963 . DOI: 10.11872/j.issn.1005-2518.2023.06.120

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http://www.goldsci.ac.cn/article/2023/1005-2518/1005-2518-2023-31-6-953.shtml

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