工矿自动化2024，Vol.50Issue(6)：61-69,9.DOI:10.13272/j.issn.1671-251x.2024050006
基于改进YOLOv8的煤矿输送带异物检测
Foreign object detection of coal mine conveyor belt based on improved YOLOv8
摘要
Abstract
The existing deep learning based foreign object detection models for conveyor belts are relatively large and difficult to deploy on edge devices.There are errors and omissions in detecting foreign objects of different sizes and small objects.In order to solve the above problems,a foreign object detection method for coal mine conveyor belts based on improved YOLOv8 is proposed.The depthwise separable convolution,squeeze-and-excitation(SE)networks are used to reconstruct the Bottleneck of the C2f module in the YOLOv8 backbone network as a DSBlock,which improves the detection performance while keeping the model lightweight.To enhance the capability to obtain information from objects of different sizes,an efficient channel attention(ECA)mechanism is introduced.The input layer of ECA is subjected to adaptive average pooling and adaptive maximum pooling operations to obtain a cross channel interactive MECA module,which enhances the global visual information of the module and further improves the precision of foreign object recognition.The method modifies the 3 detection heads of YOLOv8 to 4 lightweight small object detection heads to enhance sensitivity to small objects and effectively reduce the missed and false detection rates of small object foreign objects.The experimental results show that the improved YOLOv8 achieves a precision of 91.69％,mAP@50 reached 92.27％,an increase of 3.09％and 4.07％respectively compared to YOLOv8.The detection speed of improved YOLOv8 reaches 73.92 frames/s,which can fully meet the demand for real-time detection of foreign objects on conveyor belts in coal mines.The improved YOLOv8 outperforms mainstream object detection algorithms such as SSD,Faster-RCNN,YOLOv5,and YOLOv7-tiny in terms of precision,mAP@50,number of parameters,weight size,and number of floating point operations.关键词
输送带异物检测/YOLOv8/SE网络/高效通道注意力机制/轻量化/小目标检测/自适应平均池化/自适应最大池化Key words
foreign object detection on conveyor belts/YOLOv8/SE network/efficient channel attention mechanism/lightweight/small object detection/adaptive average pooling/adaptive maximum pooling分类
矿业与冶金引用本文复制引用
洪炎,汪磊,苏静明,汪瀚涛,李木石..基于改进YOLOv8的煤矿输送带异物检测[J].工矿自动化,2024,50(6):61-69,9.基金项目
国家重点研发计划项目(2021YFD2000204) （2021YFD2000204）
国家自然科学基金项目(12304236,32301688,52174141) （12304236,32301688,52174141）
安徽数字农业工程技术研究中心开放项目(AHSZNYGCZXKF021) （AHSZNYGCZXKF021）
大学生创新创业基金项目(202210361053,202310361037) （202210361053,202310361037）
安徽理工大学研究生创新基金项目(2024cx2067). （2024cx2067）
