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.

Stereo vision is an important branch of the research area in computer vision. Among the techniques of stereo vision, binocular stereo vision which is based on processing two images remains a research hotspot. Binocular stereo vision directly simulates the manner of human eyes observing one scene from two different viewpoints. By using the principle of triangulation, the disparities of a number of 3D points mapped to pixels in two images are computed. Then the visual information of depth is also recovered. The object surface shape can be acquired using these disparities. In this paper, a pair of common web cameras is used to collect images. Based on OpenCV, the calibration algorithm of stereo vision is achieved. Stereo rectify and stereo matching algorithm are also rapidly and efficiently implemented. Finally, the depth information of object is obtained.