Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects

Conventional deep-learning-based inspection methods for sewer pipeline defects neglect the complex inner environment of pipelines (e.g., fog and motion blur) and real-time segmentation despite their high accuracy for clear images. To solve the problem of low accuracy and slow speed of fuzzy image inspection, a novel defogging, deblurring, and real-time segmentation system for sewer pipeline defects is proposed. First, an attention-based algorithm for defogging and a generative adversarial network (GAN) for deblurring are created to improve the sharpness of pipeline images. Second, a real-time segmentation network called Pipe-Yolact-Edge is proposed to detect the defects in pipeline images at a pixel level, which achieves the highest mean average precision (mAP) of 92.65% and the fastest speed of 41.23 frames per second (fps) among the state-of-the-art segmentation networks. Comparison experiments show that the mAP of the proposed segmentation model is improved by 7.93% and 15.43% after defogging and deblurring of pipeline images, respectively, thus revealing the impact of Pipe-Defog-Net and Pipe-Deblur-GAN. In particular, the proposed defogging and deblurring methods for pipeline images can reduce the effects of contrast reduction, boundary enlargement, and missing inspection of small defects caused by fog and motion blur. Finally, the trained model is transferred into a small development device to segment the images of pipeline defects on site.