Detection method of tubular target leakage based on deep learning

Donghao Luo; Dong Wang; Huimin Guo; Xiaoxia Zhao; Meiling Gong; Lin Ye

doi:10.1117/12.2587554

12 March 2021 Detection method of tubular target leakage based on deep learning

Donghao Luo, Dong Wang, Huimin Guo, Xiaoxia Zhao, Meiling Gong, Lin Ye

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 1176384 (2021) https://doi.org/10.1117/12.2587554
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

Aiming at the problem that the previous tubular target leak detection method is not sensitive to small leakage and slow leakage and false alarm caused by external interference is not strong, tubular target leakage detection method based on deep learning is proposed. Firstly, the target detection network YOLO3 model is used to detect the tubular target in optical images. By analyzing the test results, the YOLO3 based tubular target leakage detection network is optimized and improved from three aspects: data set expansion, detection mode and network structure. Including: 1) using data transformation using rotation transformation, color dithering, zoom transformation, shift transformation and flip transformation on the data set; 2) according to the characteristics of the tubular target images, the detection method of polygon frame selection is used; 3) simplifying the network structure of the detection and output part. Finally, the improved network is trained and verified. The experimental results show that compared with the YOLO3 network model, the recognition accuracy and recall rate of the tubular target and the leaked area are greatly improved, and the average detection time is also reduced.

Citation Download Citation

Donghao Luo, Dong Wang, Huimin Guo, Xiaoxia Zhao, Meiling Gong, and Lin Ye "Detection method of tubular target leakage based on deep learning", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 1176384 (12 March 2021); https://doi.org/10.1117/12.2587554

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