基于改进YOLOv5s的煤矿输送带异物检测算法  

An Improved YOLOv5s-based Foreign Object Detection Algorithm for Coal Mine Conveyor Belts

作　　者：张炳建 郝洪涛[1] 刘秀娟[1] 李泽旭 ZHANG Bingjian;HAO Hongtao;LIU Xiujuan;LI Zexu(School of Mechanical Engineering,Ningxia University,Yinchuan 750021,China)

机构地区：[1]宁夏大学机械工程学院,宁夏银川750021

出　　处：《宁夏工程技术》2024年第2期174-179,共6页Ningxia Engineering Technology

基　　金：宁夏回族自治区重点研发计划项目(2023ZDYF0142);宁夏自然科学基金项目(2021AAC03046)。

摘　　要：为了解决现有煤矿输送带异物检测模型存在的参数量大、占用计算机资源多、检测异物种类少等问题,对YOLOv5s目标检测算法进行了优化。首先,将轻量化卷积神经网络ShuffleNetv2作为YOLOv5s骨干网络并对异物图像进行了特征提取,进而减少了模型参数量,提高了网络并行度;其次,将双向特征金字塔网络作为特征融合网络,融合了不同特征图尺度中的细节信息;最后,添加了坐标注意力机制,增强了特征提取能力,强化了异物目标关注度,从而提高了网络模型检测精度。实验结果显示,与原始模型相比,基于改进YOLOv5s的目标检测网络模型其参数量压缩为3.60×106个,检测帧率提升了8.4%,表明该算法能够在计算资源较少的情况下实现快速、准确的煤矿输送带异物检测。In order to address the issues of large parameter size,high demand for computer resources,and limited types of detected foreign objects in existing detection models for coal mine conveyor belts,YOLOv5s object detection algorithm was optimized.Firstly,a lightweight convolutional neural network,ShuffleNetv2,was adopted as the backbone network of YOLOv5s to extract features from foreign object images,reducing the parameters of the model and improving network parallelism.Secondly,a bidirectional feature pyramid network was used as the feature fusion network to integrate detailed information from different feature map scales.Finally,a coordinate attention mechanism was added to enhance the feature extraction capability,strengthen the focus on foreign object targets,and thereby improve the detection accuracy of network model.Experimental results show that compared to the original model,the improved YOLOv5s-based object detection network model achieved a parameter compression of 3.6×106,and an 8.4%increase in detection frame rate,demonstrating that this method can achieve rapid and accurate foreign object detection on coal mine conveyor belts with fewer computational resources.

关 键 词：煤矿输送带 异物检测 YOLOv5s 特征融合网络 注意力机制 

分 类 号：TD528[矿业工程—矿山机电] TP183[自动化与计算机技术—控制理论与控制工程] TP751[自动化与计算机技术—控制科学与工程]