Li Yuanyuan, Song Liyuan, Cai Yin, Fang Zhijun, Tang Ming
Shanghai University of Engineering Science, Songjiang, Shanghai, 201620, China.
Sci Rep. 2024 Mar 6;14(1):5537. doi: 10.1038/s41598-023-50671-7.
In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites.
在工业领域，缺陷分类和缺陷定位任务是缺陷检测系统的重要组成部分。然而，现有研究仅专注于其中一项任务，难以保证两项任务的准确性。本文提出了一种基于改进的Yolo_v4的缺陷检测系统，该系统大大提高了对微小缺陷的检测能力。针对K_Means算法聚类先验框主观性强的问题，本文提出基于密度的带噪声空间聚类（DBSCAN）算法来确定锚框数量。为了解决CSPDarknet53特征提取网络中低级特征复用率不足导致小目标检测率低的问题，本文提出了一种ECA-DenseNet-BC-121特征提取网络对其进行改进。并且提出了双通道特征增强（DCFE）模块，以改善PANet网络中四链卷积导致的局部信息损失和梯度传播阻碍，从而提高模型的鲁棒性。在织物表面缺陷检测数据集上的实验结果表明，改进后的Yolo_v4的平均精度均值（mAP）为98.97%，比SSD高7.67%，比Faster_RCNN高3.75%，比Yolo_v4 tiny高10.82%，比Yolo_v4高5.35%，检测速度达到39.4帧每秒。它能够满足工业现场的实时监测需求。
