# Image Recognition Method for Micropores Inside Small Gas Pipelines
## 6 Conclusions  
In this paper, to address the problem related to difficulties in the early detection of leakage in small gas pipelines, a method is proposed for image recognition of micropores inside small gas pipelines, which is used to recognize leakage signs such as micropores that appear in the early stage of pipeline leakage. The steps included collecting images, building data sets, and introducing the BiFPN structure into YOLOv5s to improve the feature fusion ability of the network model. For the problem of difficult-to-recognize micropores of tiny size, we built a small target detection layer in the Head layer to recognize such tiny micropores as 1 mm. Then, we used the training set to train the network model for recognizing micropores inside small gas pipes and used the test set of micropores with different shapes and sizes to verify the model's recognition ability. The current mainstream target detection algorithms were compared to prove the superiority of this method. Ablation experiments were carried out to prove that the recognition ability of the method in this paper was improved due to the improvements made. The experiment proved that the small gas pipeline microporosity recognition method proposed in this paper has a better recognition effect on the early leakage characteristics of small gas pipelines and can recognize leakage characteristics at the early stage of pipeline leakage to realize early safety warnings with a precision rate of 94.7%, a detection rate of 96.6%, and an average precision of 95.5%. The experimental results show that this method can avoid the disadvantages of time-consuming and high-cost large-area excavations in the routine maintenance of pipelines for identifying internal micropores in small gas pipelines. The method can still maintain good identification results when other pipeline leakage identification methods have poor identification results.  
Conceptualization, Y.Z., Z.S., H.Z. and J.L.; methodology, Y.Z., Z.S., H.Z., and J.L.; software, Y.Z.; validation, Y.Z., Z.S., H.Z. and J.L.; formal analysis, Y.Z., Z.S., H.Z. and J.L.; investigation, Y.Z., Z.S., H.Z. and J.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., Z.S., H.Z. and J.L.; supervision, Y.Z., Z.S., H.Z. and J.L.; project administration, Y.Z., Z.S., H.Z. and J.L.; funding acquisition, Z.S. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No. 2020 YFC1511702, 2022 YFF0607400), the National Natural Science Foundation of China (61971048), Beijing Science and Technology Project (Z221100005222024), and the Beijing Scholars Program, Key Laboratory of Modern Measurement and Control Technology, Ministry of Education.  
Data Availability Statement: Not applicable.  
Acknowledgments: The authors would like to express their sincere thanks and appreciation to their supervisor, [PERSON], for his support, comments, suggestions, and engagement during the writing of this manuscript. In addition, the authors would like to thank the editor and the anonymous reviewers for their constructive comments on improving the content during the publishing process of this paper.