2023 Volume 4 Issue 3 Pages 571-581
A method of inspecting the condition of utility poles from images acquired by vehicle-mounted cameras will be effective at reducing the cost of maintenance and management because it will reduce the need for inspectors to visit utility poles onsite. In this paper, we propose a convolutional neural network-based image recognition method for classifying the locations of utility poles from roadside images. The proposed method has a unique structure that combines an object detection model that detects utility poles and the areas of public and private land at the pixel level with a relationship recognition model that classifies the locations of utility poles into three categories: public land, private land, or cannot be determined (confirmation required). In the verification results, 242 utility poles were detected with an accuracy of 96.7%, and the locations were categorized with an accuracy of 84.2%, with a particularly high accuracy rate of 91.9% (Recall) for public land. The proposed method can be fully utilized for the primary judgment before the final judgment by the inspector.
