Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents while operating at low altitude. Even though many studies have been conducted to detect transmission lines, there still remains many challenges due to their thin shapes in diverse backgrounds. Moreover, most previous methods require a significant level of human involvement to generate pixel-level ground truth data. In this paper, we propose a transmission line detection algorithm based on weakly supervised learning and unpaired image-to-image translation. The proposed algorithm only requires image-level labels, and a novel attention module, which is called parallel dilated attention (PDA), improves the detection accuracy by recalibrating channel importance based on the information from various receptive fields. Finally, we construct a refinement network based on unpaired image-to-image translation in order that the prediction map is guided to detect line-shaped objects. The proposed algorithm outperforms the state-of-the-art method by 2.74% in terms of F1-score, and experimental results demonstrate that the proposed method is effective for detecting transmission lines in both quantitative and qualitative aspects.