改进YOLOv4的输送带纵向撕裂检测  

Longitudinal Tear Detection Based on Improved YOLOv4 for Conveyor Belt

作　　者：秦彤 李晓明[1] QIN Tong;LI Xiao-Ming(College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学计算机科学与技术学院,太原030024

出　　处：《计算机系统应用》2023年第3期186-194,共9页Computer Systems & Applications

基　　金：国家自然科学基金(61373099)。

摘　　要：输送带纵向撕裂检测是煤矿安全生产的重要问题之一.针对矿用输送带纵向撕裂检测存在因数据量不足、损伤形态多样化、极致宽高比而导致的检测精度不足、存在误检与漏检等问题,本文提出一种改进YOLOv4的输送带纵向撕裂检测算法.首先,通过数据增强的方式扩充现有数据,构建输送带纵向撕裂数据集.其次,在主干网络之中添加可变形卷积,增强模型对多样化损伤形态的特征提取能力.最后,在特征融合阶段,引入跨阶段局部网络(CSPNet)结构,提升模型对极致宽高比的纵向撕裂检测性能,进一步降低模型的漏检与误检.实验结果表明,输送带纵向撕裂检测准确率达到92.5%, F1分数达到93.1%,基本满足输送带纵向撕裂检测要求.Longitudinal tear detection of conveyor belts is one of the important issues in coal mine safety production. In the longitudinal tear detection of mining conveyor belts, insufficient detection accuracy, false detections, and missing detections occur due to insufficient data, diversified damage patterns, and extreme aspect ratios. In this study, an improved YOLOv4 longitudinal tear detection algorithm for conveyor belts is proposed. First, the existing data is expanded by data enhancement to construct a longitudinal tear data set for conveyor belts. Secondly, the variable convolution is added to the backbone network to enhance the feature extraction ability of the model for diverse damage patterns. Finally, in the feature fusion stage, the cross-stage partial network(CSPNet) structure is introduced to improve the longitudinal tear detection performance of the model for extreme aspect ratios, and further reduce missing detection and false detection. The experimental results show that the accuracy of the longitudinal tear detection for the conveyor belt reaches 92.5%, and the F1 score reaches 93.1%, which basically meets the requirements of the longitudinal tear detection for the conveyor belt.

关 键 词：输送带纵向撕裂检测 YOLOv4 数据增强 可变形卷积 跨阶段局部网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] TD528.1[矿业工程—矿山机电]