Attentional Information Fusion Networks for Cross-Scene Power Line Detection

Abstract

The power line is one of the most hazardous obstacles for low-altitude aircrafts. As aircrafts usually encounter scenes like never before during the flight, cross-scene power line detection is the key for their flight safety. However, compared to regular object detection tasks, cross-scene power line detection is extremely challenging due to its weak visual appearance and widespread existence. In this letter, we propose a cross-scene power line detection method based on attentional information fusion networks. Specifically, we construct a fully convolutional network with attention and information fusion mechanism for cross-scene detection. The two main modules make full use of the semantic and location information, which enables the model to focus more on power lines rather than the unexpected scenes. To the best of author knowledge, our method establishes the first end-to-end convolutional architecture for pixelwise power line detection. Experimental results have shown that our method outperforms previous methods by large margins for cross-scene power line detection.