CHALKING DETECTION OF HEADRACE TUNNEL BY YOLOv5

Abstract

The deteriorated infrastructures constructed during the high economic growth period result in the need for enormous maintenance and management costs. Maintenance of the headrace tunnel using AI technology is desired in view of the points that the difficulty to deploy the large machine and water is cut off during inspection. In this study, chalking points on the inner wall of the headrace tunnel were detected by YOLOv5 in consideration of these situations. As a result, the chalking points can be detected with high accuracy by using the multiplier or clear data. In addition, it will be possible to investigate the causes of deterioration and propose the repair plans by understanding the distribution of deteriorated areas based on the detection results since a part of the chalking area can be captured even the IoU is low or the detection is not accurate. For the classes that were not detected, it is necessary to consider the augmentation of training data for infrequently occurring deterioration or use the classification model added to the model used in this study in order to improve classification accuracy.