基于改进ShuffleNetV2的轻量级花色布匹瑕疵检测

布匹瑕疵检测是纺织业质量管理的重要环节.在嵌入式设备上实现准确、快速的布匹瑕疵检测能有效降低成本,因而价值巨大.考虑到实际生产中花色布匹瑕疵具有背景复杂、数量差异大、极端长宽比和小瑕疵占比高等结构特性,提出一种基于轻量级模型的花色布匹瑕疵检测方法并将其部署在嵌入式设备Raspberry Pi 4B上.首先在一阶段目标检测网络YOLO的基础上用轻量级特征提取网络ShuffleNetV2提取花色布匹瑕疵的特征,以减少网络结构复杂度及参数量,提升检测速度;其次是检测头的解耦合,将分类与定位任务分离,以提升模型收敛速度;此外引入CIoU作为瑕疵位置回归损失函数,提高瑕疵定位准确性.实验结果表明,本文算法在Raspberry Pi 4B上可达8.6 FPS的检测速度,可满足纺织工业应用需求.

Lightweight Colored Fabric Defect Detection Based on Improved ShuffleNetV2

Fabric defect detection is an important link in the quality management of the textile industry. Accurate and fast fabric defect detection on embedded devices can effectively reduce the detection cost, thus being of great value. Considering the structural characteristics of colored fabric defects in actual production, such as a complex background, large differences in the quantity of defects, an extremely high aspect ratio, and a high proportion of small defects, a colored fabric defect detection method based on a lightweight model is proposed and implemented on an embedded circuit board Raspberry Pi 4B. The lightweight feature extraction network ShuffleNetV2 is first used to extract the features of colored fabric defects on the basis of the one-stage target detection network, you only look once (YOLO), so as to reduce the complexity of the network structure and the number of parameters and improve the detection speed. Then, the detection head is decoupled to separate the classification and localization tasks so that the convergence speed of the model can be improved. In addition, the complete intersection over union (CIoU) is introduced as the loss function of defect location regression to improve the accuracy of defect location. The experimental results show that the proposed algorithm can achieve a detection speed of 8.6 FPS on Raspberry Pi 4B, which can meet the needs of the textile industry.