# Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
###### Abstract
Although the detection effect is improved compared with the traditional methods, the basic deep learning model is incompetent for high-precision detection tasks such as Drone-captured Scenarios [20], and the actual detection effect cannot meet the requirements of accurately locating defects. Therefore, replacing the basic model with a classical model which has a more stable detection effect is the research direction in recent years. [PERSON] et al. [21] proposed a faster region-based convolutional neural network with a visual gain mechanism (Faster VG-RCNN). By analyzing the relationship between the attention mechanism and the visual gain mechanism, it was found that the attention-related visual gain mechanism can change the corresponding magnitude without changing the selectivity, and improve visual perception. Compared with Faster-RCNN, the experimental results show that the detection accuracy is improved by about 4%. [PERSON] et al. [22] proposed a fabric defect detection algorithm based on improved YOLOv3, first using the K-Means algorithm to determine the suitable anchors, then combining the low-level and high-level information, and adding the Yolo detection layer to the feature maps of different sizes. After the improvement, the false detection rate for specific types of fabric is less than 5%. [PERSON] et al. [23] proposed a fabric defect detection algorithm based on improved Yolov5. The Adaptive Spatial Feature Fusion (ASFF) method is used to improve the bad effect of Path Aggregation Network (PANet) on multi-scale features, and an attention mechanism is added to make the network focus on useful information. The final experimental results show that the average accuracy rate in fabric defect detection is 71.70%. [PERSON] et al. [24] proposed an improved YOLOv4 network: first, preprocess the flawed image to decompose the image into smaller sizes, and then use the filtering method to denoise the flawed features to enhance the robustness of the model, then deploy the trained model to the hardware; the accuracy of detecting specific flaws reached 95.3%. [PERSON] et al. [25] proposed a capsule Network, unlike the traditional CNN, which causes information loss, the capsules are capable of holding more information and are a group of neurons that include not only the probability of a particular object's presence, but also different informative values related to instantiation parameters. The result shows the CapsNet achieved a performance value of 98.7%. [PERSON] et al. [26] proposed SE-YOLOv5 network, adding the Squeeze-and-Excitation Networks (SE) [27] module to the backbone network of YOLOv5 and replacing the ReLU activation function in the Cross Stage Partial (CSP) module with the Activate or Not (ACON) [28] activation function. Experimental results show that the accuracy, generalization ability, and robustness are improved. After using the classic model and improving it, it can be seen that the accuracy of the detection results has been greatly improved, and the requirements of accurate positioning of defects can be achieved when detecting ordinary defect targets, however, when encountering tiny targets, the detection effect will be greatly reduced. In the field of tiny target detection, [PERSON] et al. [29] proposed an improved YOLOv4 network to detect tiny targets in transmission line faults, adding an attention module combining ECAM (Efficient Channel Attention Module) and CBAM (Convolutional Block Attention Module) into the backbone network, using the clustering algorithm to regenerate Anchors, and, using a new loss function, the mAP50 for tiny target transmission line fault detection reaches 95.16%. [PERSON] et al. [30] improved the YOLOv3 network for tiny target prediction problems; the feature map output extracted by the second-layer residual network of the backbone is fused with the feature map output of other residual layers to form a new feature prediction layer, and the anchors are clustered using a clustering algorithm. Great results have been achieved in tiny target prediction from satellite image.