Leather Defect Detection Algorithm Based on Improved YOLOv5

doi:10.19678/j.issn.1000-3428.0064587

Abstract

Abstract:

In the industrial leather production industry, leather defect detection is an important part of quality control. Aiming at the problems of high computational complexity, difficulties in small target defect detection, and few data samples, this study proposes an improved lightweight leather defect detection algorithm based on attention mechanism, GPC-YOLOv5. Defect pictures are collected using an industrial camera; whereby the images are annotated to create a defect dataset, and use the YOLOv5s model for object detection.The number of original datasets are expanded to solve the problem of few data samples using the Mosaic method combined with ImgAug data augmentation in the training process.The lightweight GhostNet module replaces the convolution module of the backbone network and neck in YOLOv5, thereby effectively reducing the number of parameters and computation load of the model.The network complexity reduces and calculations accelerate because of improving the activation function and real-time requirements are satisfied. The feature extraction ability of the network for small target defects is enhanced by adding a new attention mechanism, Polarized Self-Attention module, to the backbone network.The experimental results show that compared to YOLOv5, the GPC-YOLOv5 algorithm reduces the number of parameters and computation load by 25.4% and 28.5%, respectively, with an overall mean Average Precision(mAP) of 89.2%, effectively improving detection accuracy and accelerating detection speed.

Key words: leather defect, lightweight YOLOv5 algorithm, attention mechanism, deep learning, object detection

摘要：

皮革瑕疵检测是工业皮革生产行业中质量控制的重要环节，针对工业皮革瑕疵在线检测中存在计算复杂度高、对小目标检测效果差、数据样本少等问题，提出一种基于注意力机制的轻量化皮革瑕疵检测算法GPC-YOLOv5。使用工业相机采集瑕疵图片并对其进行标注，制作瑕疵数据集，利用YOLOv5s模型进行目标检测。使用ImgAug数据增强技术扩充原始数据集的数量，并在训练过程中结合Mosaic数据增强方法解决数据样本少的问题。在YOLOv5的基础上，使用轻量化的GhostNet模块替换主干网络和颈部的卷积模块，有效减少模型的参数量和计算量，通过改进激活函数减少网络复杂度并加快计算速度，以满足实时性需求。在主干网络中加入新型注意力机制Polarized Self-Attention模块，增强网络对于小目标瑕疵的特征提取能力。实验结果表明，相比YOLOv5，GPC-YOLOv5算法的参数量和计算量分别减少25.4%和28.5%，总体mAP达到89.2%，能够有效提高检测精度并加快检测速度。

关键词: 皮革瑕疵, 轻量型YOLOv5算法, 注意力机制, 深度学习, 目标检测

Junhao LIU, Meilin WANG, Xing XIE, Yexing SONG, Lihua XU. Leather Defect Detection Algorithm Based on Improved YOLOv5[J]. Computer Engineering, 2023, 49(8): 240-249.

刘俊豪, 王美林, 谢兴, 宋烨兴, 许莉花. 基于改进YOLOv5的皮革瑕疵检测算法[J]. 计算机工程, 2023, 49(8): 240-249.

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Figures/Tables 18

References 31

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数据集	图片数/张	气泡状/个	孔洞状/个	条纹状/个	Label总数/个
训练集	636	1 252	807	484	2 543
测试集	158	292	178	125	595
总计	794	1 544	985	609	3 138

数据集	图片数/张	气泡状/个	孔洞状/个	条纹状/个	Label总数/个
训练集	636	1 252	807	484	2 543
测试集	158	292	178	125	595
总计	794	1 544	985	609	3 138

模型	FLOPs/10⁹	参数量/10⁶	模型大小/MB
YOLOv5	15.8	7.02	13.8
Ghost-YOLOv5	8.1	3.68	7.5

模型	FLOPs/10⁹	参数量/10⁶	模型大小/MB
YOLOv5	15.8	7.02	13.8
Ghost-YOLOv5	8.1	3.68	7.5

模块	FLOPs/10⁹	参数量/10⁶	召回率	F1值	mAP/%
Ghost-YOLOv5	8.1	3.68	0.786	0.834	86.0
Ghost-YOLOv5+CBAM	10.0	4.50	0.782	0.838	86.6
Ghost-YOLOv5+SENet	10.0	4.54	0.804	0.850	87.7
Ghost-YOLOv5+ParNetAttention	18.6	8.29	0.811	0.852	87.8
Ghost-YOLOv5+CotAttention	17.3	7.51	0.793	0.839	86.6
Ghost-YOLOv5+ECAttention	9.9	4.45	0.815	0.858	88.3
Ghost-YOLOv5+Polarized Self-Attention	11.3	5.16	0.809	0.859	88.9
Ghost-YOLOv5+EIoU	8.1	3.68	0.754	0.811	84.4
Ghost-YOLOv5+AlaphIoU (α=3)	8.1	3.68	0.755	0.821	83.4
Ghost-YOLOv5+CDIoU	8.1	3.68	0.788	0.839	86.5
Ghost-YOLOv5+Polarized Self-Attention+EIoU	11.3	5.16	0.800	0.845	87.8
Ghost-YOLOv5+Polarized Self-Attention+AlaphIoU	11.3	5.16	0.787	0.844	86.4
Ghost-YOLOv5+ECAttention+CDIoU	9.9	4.45	0.817	0.860	88.7
Ghost-YOLOv5+Polarized Self-Attention+CDIoU	11.3	5.16	0.819	0.867	89.2

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