# Image Recognition Method for Micropores Inside Small Gas Pipelines
## 4 Results
### Analysis of Comparative Experimental Results  
To compare the recognition effect of the micropores recognition network model inside the small gas pipeline proposed in this paper with the YOLOv5s network model, the test set was input to the trained micropore recognition network model inside the small gas pipeline and the YOLOv5s network model, respectively, to compare the micropore recognition effect of the two network models. The recognition results are shown in Figure 15.  
From Figure 15, it can be seen that there are various leak shapes, such as pore-like structures, strip-like structures, and irregular shapes. Microores have different sizes, and the smallest size is the pore-like micropores in Figure 15a,b, which reach 1 mm in diameter. Figure 15a,b show pictures of the results using the YOLOv5s network model and the network model proposed in this paper for the identification of micropores inside a small gas pipe for the same test set image 1, respectively. Figure 15c,d show pictures of the results from the recognition of the same test set image 2 using the YOLOv5s network  
Figure 14: Training curves for the four network models. model and the network model for identifying micropores inside small gas pipes proposed in this paper, respectively. From the above results, it can be seen that the YOLOv5s network model may mistakenly identify one micropore as two micropores, and the phenomenon of missing detection may occur. However, the proposed small gas pipeline internal micropore identification network model does not have the above situation, and it has a higher confidence level than the YOLOv5s network model when micropores are also identified. Thus, it can be seen that the proposed network model for identifying micropores inside small gas pipelines has a better recognition effect and a greater recognition capability than the YOLOv5s network model. To prove that the recognition ability of the proposed method is better than the mainstream algorithms in the target domain, comparison experiments were conducted with one-stage target detection algorithms YOLOv5, YOLOv3 [24], and SSD [25] and the two-stage target detection algorithm Faster RCNN [26]. The test sets were input to five network models including the YOLOv5, YOLOv3, SSD, Faster RCNN, and the small gas pipeline internal micropore identification network model proposed in this paper to compare the  
Figure 15: Comparison of recognition results between YOLOv5s and the method in this article. (**a**) YOLOv5s identification results in an irregular shape and pore-like structure of micropores (image 1). (**b**) The irregular shape and pore-like structure of micropore identification results from the method proposed in this paper (image 1). (**c**) YOLOv5s identification results showing micropores with striped structure, and irregular shape (image 2). (**d**) The results showing the identification of strip structure, irregular shape, and strip structure microfine pores using the method proposed in this paper (image 2). numerical magnitude of the three evaluation indexes including P, R, and AP. The evaluation indexes of each network model comparison experiment are shown in Table 3.  
As can be seen from Table 3, the evaluation metrics of the network model in the table are precision, recall, and average precision, in that order, and the two-stage target detection algorithm is higher than the one-stage target detection algorithm in terms of precision and recall. Among the one-stage target detection algorithms, YOLOv3 has the worst results. SSD is 0.7, 0.4, and 0.5 percentage points higher than YOLOv3 in terms of precision, recall, and average precision, respectively. YOLOv5s is 1.1, 1.3, and 1.2 percentage points higher than SSD in terms of precision, recall, and average precision, respectively, and is the most effective of the one-stage detection algorithms. Compared with YOLOv5s, Faster RCNN is 2.2 percentage points, 5.8 percentage points, and 4.1 percentage points higher in terms of precision, recall, and average precision, respectively.