Edward Rowe Aleluya

Power outages impede economic operations, because numerous processes rely on electricity. Doing regular maintenance and inspection is one solution. However, traditional foot patrol inspection methods can be risky, slow, and tedious. An innovative contemporary alternative involves inspecting distribution networks by unmanned aerial vehicles (UAVs). Thus, this paper introduces a three-tier inspection procedure: track power lines, detect utility assets (transformer bank, low-voltage bushing, high-voltage bushing, arrester, radiator fin, and cutoff fuse), and spot foreign object obstructions (snake, gecko, and tree branch). This work exhibits two contributions. First, it introduces new learning representations for detecting power lines, utility assets, and foreign obstructions. The image processing-based techniques are built by modifying pre-trained neural networks – Point Instance Network (PINet) for power lines and You Only Look Once (YOLO) for utility assets and obstructions. Second, it presents trained detection models with image datasets tailored to each problem. The datasets consist of images from both obstruction-free and shelled UAVs for the three detection tasks. Experimental results indicate no significant disparity in utilizing two distinct UAV variants, with or without a shell. The proposed method, leveraging Transfer Learning with PINet, yields around 80% accuracy and 72% f1-score across obstruction-free and obstructed image datasets. Moreover, the retrained YOLO checkpoints with yolov5m and yolov5l weights showcase suitability for deployment in detecting utility assets and foreign object obstructions that achieved mAPs around 0.96 and 0.85, respectively. Despite a slight decline in performance metrics with obstructed images, the models signify a balance between detection accuracy and the safety conferred by the insulated shell.