# Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
###### Abstract  
Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the field of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classification and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect. fabric defect; YOLOv4; CBAM; CEIOU; tiny target +
Footnote †: journal: Applied  
0000-0001-999-2233-2233-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-2333-23333-23333-2333-2333-2333-2333-23333-2333-23333-2333-  
backgrounds, differences in the production environment of enterprises, and classification of various fabric defects have always restricted the accuracy and speed of automatic detection of fabric defects. In recent years, experts and scholars have conducted research in this field, and many different detection methods have been produced. The three most mainstream categories of these methods are: spectrum analysis [2; 3], model analysis [4; 5] and deep learning-based methods [6; 7; 8]. Although most industrial production currently uses the first two methods, the deep learning-based methods have greatly surpassed them in terms of detection speed and detection accuracy, and have begun to be applied to industrial fabric defect detection on a large scale. Since its introduction, deep learning has been widely used in related research, such as in the medical field [9; 10; 11; 12; 13], various types of target recognition [14; 15; 16] and natural language processing [17], and has achieved quite good results in these fields. Therefore, using the deep learning method to detect fabric defects will result in more accurate detection results. In the aspect of using a basic deep learning model on fabric detection, [PERSON] et al. [18] proposed a data description model based on support vector; the optimal Gaussian kernel parameters are selected during training to solve the shortcomings of fabric defects that cannot be handled by a single fractal, however, this method is not particularly effective for image classification with strong textures. [PERSON] et al. [19] proposed an efficient convolutional neural network for defect segmentation and detection; this framework can significantly alleviate the amount of pictures needed while training the neural network and obtain the location of defects with high accuracy. The results show that this network significantly outperforms eight state-of-the-art methods in terms of accuracy and robustness. The methods introduced above use deep learning-based models.