# Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO
## 5 Discussion  
### Analysis of Experiment Results  
By comparing the experimental results, we can find out the quality of each model's detection effect on this dataset and the advantages of this paper's model in fabric defect detection. Through Table 2, it can be seen that the algorithm in this paper has a great improvement in the overall mAP compared with the basic algorithm, with an average increase of about 4 percentage points, which can more accurately detect the fabric defects existing in the image. Through Table 3, it can be seen that although the overall mAP improvement is only about 2 percentage points, the algorithm in this paper has great advantages in the detection results of tiny targets. Compared with other detection algorithms for tiny targets, the detection accuracy of the three groups of tiny targets is improved by about 8%, which further highlights the accuracy of the algorithm in this paper for tiny target detection. As can be seen from the Table 4, the robustness of the model is increased due to the expansion of the training set due to data augmentation, which slightly increases the detection capability of the network. The added attention mechanism has a significant impact on the detection ability of the model. The reason is that the original algorithm does not have a focus on feature extraction and will be interfered with by many irrelevant factors. After adding an attention mechanism, the model is more effective in extracting significant features of fabric defects. After adding a feature output layer and using the new yolo_anchors, the overall mAP of the model has been significantly improved, and the detection of tiny targets has made a groundbreaking improvement. The AP value of tiny \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Data Aug**} & \multirow{2}{*}{**CBAM**} & \multicolumn{2}{c}{**New-**} & \multicolumn{2}{c}{**AP50**} & \multirow{2}{*}{**mAP**} \\  & & & & **Anchors** & & **Hole** & **Knot** \\ \hline model-1 & \(\surd\) & - & - & 66.20\% & 57.36\% & 53.80\% \\ model-2 & \(\surd\) & \(\surd\) & - & 71.84\% & 60.59\% & 54.60\% \\ Model-3 & - & \(\surd\) & \(\surd\) & 77.35\% & 66.42\% & 55.72\% \\ model-4 & \(\surd\) & \(\surd\) & \(\surd\) & 81.20\% & 69.49\% & 56.58\% \\ \hline \hline \end{tabular} Among them, New-Anchors means adding a new feature output layer and using new yolo_anchors, \(\surd\) means adding the corresponding module, \(\prime\)–\(\prime\) means not adding the corresponding module.  
\end{table} 
Table 4: Comparison of improved module. Figure 9: Comparison of CEIOU and EIOU in training. target detection has increased by about 9% which greatly enhanced the model's ability to detect tiny objects in fabric defects. In order to more intuitively reflect the detection performance of the improved object detection algorithm, first use the original YOLOv4 algorithm to detect the input image, and then use the improved YOLOv4 algorithm to detect the same image. Figures 10-12 shows the results of YOLOv4 and the improved YOLOv4 after detecting the same picture, where (a) is the detection result of the YOLOv4 algorithm, and (b) is the detection result of the improved YOLOv4 algorithm. In Figure 10, a picture of defects containing 4 defects is used, and these 4 defects are all tiny target knots. After using the YOLOv4 algorithm to detect the image, as (a1) shows that the tiny target has missed detection obviously, only 2 of 4 defects were detected. However, the images detected by the algorithm in this paper perfectly find out all the tiny targets as (b1) shown. Figure 11: Experimental results of group2. (**a2**) detection result of YOLOv4; (**b2**) detection result of improved YOLOv4. Defect sizes are about 4 \(\times\) 12 pixels. Figure 10: Experimental results of group1. (**a1**) detection result of YOLOv4; (**b1**) detection result of improved YOLOv4. Defect sizes are about 3 \(\times\) 8 pixels. In Figure 11, a flawed picture containing 4 tiny targets is used, and these 4 defects are all tiny target knots.