Fabric defect detection using the improved YOLOv3 model

Abstract

To improve the detection rate of defect and the fabric product quality, a higher real-time performance fabric defect detection method based on the improved YOLOv3 model is proposed. There are two key steps: first, on the basis of YOLOv3, the dimension clustering of target frames is carried out by combining the fabric defect size and k-means algorithm to determine the number and size of prior frames. Second, the low-level features are combined with the high-level information, and the YOLO detection layer is added on to the feature maps of different sizes, so that it can be better applied to the defect detection of the gray cloth and the lattice fabric. The error detection rate of the improved network model is less than 5% for both gray cloth and checked cloth. Experimental results show that the proposed method can detect and mark fabric defects more effectively than YOLOv3, and effectively reduce the error detection rate.

A Robust Fabric Defect Detection Method Based on Improved RefineDet

“…The raw images used for recent research were mainly from public datasets or collected by textile factories and laboratories. Some typical public defect detection datasets are TILDA dataset ( ), DAGM2007 dataset ( ), and Hong Kong patterned texture database ( ); and some self-built datasets are DHU-FD-500 [ 7 ], DHU-FD-1000 [ 7 ], lattice [ 8 ], FDBF dataset [ 19 ], etc. Image preprocessing.…”

“…The backbone as the basic feature extractor of the object detection task is used to generate the output feature maps of the corresponding input images. The common backbones are VGG-16 [ 34 , 36 ], ResNet [ 37 , 38 ], ResNeXt [ 39 ], DarkNet-19 [ 15 , 21 ], DarkNet-53 [ 8 , 16 ], MobileNet [ 40 , 41 ], and ShuffleNet [ 42 ]. Neck structure.…”

“…This means that the configuration of model parameters requires some prior knowledge or problem-specific research. When the fabric products with new design patterns appear, these detection methods must be modified or even redesigned [8].…”

“…Wei et al [22] designed three models based on YOLOv3 to detect the surface defects on magnetic tiles. Jing et al [8] introduced the YOLOv3 model to fabric defect detection and added a lower feature layer to the feature pyramid.…”

“…Some typical public defect detection datasets are TILDA dataset (https://lmb.informatik.uni-freiburg.de/resources/datasets/ tilda.en.html), DAGM2007 dataset (https://hci.iwr.uni-heidelberg.de/content/weakly-supervisedlearning-industrial-optical-inspection), and Hong Kong patterned texture database (https://ytngan. wordpress.com/codes/); and some self-built datasets are DHU-FD-500 [7], DHU-FD-1000 [7], lattice [8], FDBF dataset [19], etc.…”