Intelligent identification and location of defects in water supply pipeline based on improved YOLOX algorithm

doi:10.13474/j.cnki.11-2246.2023.0361

Abstract

Abstract: To solve the problem of difficult and slow real-time automated detection of defects in water supply pipelines, a new intelligent identification and positioning method for water supply pipelines is proposed based on a dataset of pipeline defect data collected from actual engineering projects. The new YOLOX algorithm model, which incorporates an attention module, is developed and used for algorithm training and prediction using a dataset of video frames. Test results show that the YOLOX algorithm model with attention mechanism achieved an average testing accuracy of 94%, a mAP value of 84%, and an average recognition speed of 16 m/s. Additionally, compared with three other commonly used algorithm models (YOLO V3 and Fast R-CNN), the new model showed the best overall performance. This proposed model can also be applied to real-time video detection, providing an efficient and accurate detection technology and method for the intelligent identification and positioning of defects in water supply pipelines.

Key words: defects in water supply pipeline, improved YOLOX algorithm, attention mechanism, identification and localization

CLC Number:

P258

SU Changwang, HU Shaowei, ZHANG Haifeng, PAN Fuqu, SHAN Changxi. Intelligent identification and location of defects in water supply pipeline based on improved YOLOX algorithm[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 70-75.

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