改进YOLOv5的钢丝绳损伤图像识别实验方法研究

doi:10.3778/j.issn.1002-8331.2210-0505

摘要/Abstract

摘要： 钢丝绳在煤矿设备中发挥着非常重要的作用。为了能够及早发现钢丝绳损伤，提前进行预警和故障处理，保护矿下人员安全，提出了一种基于深度学习的钢丝绳损伤识别检测方法，采用目标检测算法YOLOv5并对其进行改进。采用快速自适应加权中值滤波进行图像预处理，提高钢丝绳损伤图像识别精度，改进后运行速度提升到187?ms/张，且增强效果良好；将CBAM模块和Transformer prediction heads（TPH）集成到YOLOv5，数据集扩充后输入到改进的模型中进行训练测试。实验结果表明，改进后的模型检测性能良好，最终平均准确率达到了0.893，比原算法高了0.037，比传统检测算法SSD、Faster R-CNN以及原始YOLOv3分别高0.196、0.162、0.102，表明该算法精度较高，有效提高了钢丝绳损伤图像的识别准确率。

关键词: 钢丝绳损伤, 目标检测, 图像识别, YOLOv5

Abstract: Wire rope plays a very important role in coal mine equipment. In order to find the wire rope damage as early as possible, conduct early warning and fault handling in advance, and protect the safety of personnel under the mine, a method of wire rope damage identification and detection based on depth learning is proposed. The target detection algorithm YOLOv5 is adopted and improved. The fast adaptive weighted median filter is used for image pre-processing to improve the recognition accuracy of wire rope damage images. After the improvement, the running speed is increased to 187?ms/piece, and the enhancement effect is good. It integrates CBAM and Transformer prediction heads（TPH） into YOLOv5, and inputs the expanded dataset into the improved model for training and testing. The experimental results show that the improved model has good detection performance, and the final average accuracy rate reaches 0.893, 0.037 higher than the original algorithm, 0.196, 0.162 and 0.102 higher than the traditional detection algorithm SSD, Faster R-CNN and the original YOLOv3, respectively. It shows that the algorithm in this paper has high accuracy and effectively improves the recognition accuracy of wire rope damage images.

Key words: wire rope damage, target detection, image recognition, YOLOv5

王红尧, 韩爽, 李勤怡. 改进YOLOv5的钢丝绳损伤图像识别实验方法研究[J]. 计算机工程与应用, 2023, 59(17): 99-106.

WANG Hongyao, HAN Shuang, LI Qinyi. Experimental Research on Image Recognition of Wire Rope Damage Based on Improved YOLOv5[J]. Computer Engineering and Applications, 2023, 59(17): 99-106.

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