Automatic detection of water supply pipe defects based on underwater image enhancement and improved yolox

The water supply pipe system is an important component of the municipal pipe system. However, water supply pipes usually suffer from various defects, such as deposits and infiltrations, which severely affect their performance and result in millions of dollars being wasted on maintenance work. Therefore, timely and effective inspection of water supply pipes is very important. In recent years, automatic detection based on deep learning methods has had the advantages of high efficiency, low cost, and time saving, thus gradually replacing manual inspection for defects in the pipe system. To solve the problem of unclear image acquisition for water supply pipes, this paper proposes a novel automated detection method for water supply pipe defects, mainly involving the use of underwater image enhancement (UIE) algorithms to improve data set image quality, and an attention mechanism was utilized to improve the You Only Look Once X (YOLOX) model for defects detection. Experimental results demonstrate that the improved YOLOX model based on the data set enhanced by underwater image enhancement and attention mechanism achieved an average accuracy [mean average precision (mAP)] value of 92.4% and F1 score of 0.86, which are better than traditional models. Finally, an efficient and accurate automated detection procedure for water supply pipe defects was provided.