关键词: 目标检测, 遥感, 深度学习, Yolo v3, 图像处理

Abstract:

In order to solve the problems in building detection for remote sensing images, such as the difficulty of detecting small buildings and the lack of real-time performance in the detection process, the deep learn-based target detection algorithm Yolo v3 is applied to the building detection scene. The basic algorithm of Yolo v3 with good real-time and universality is adopted to meet the real-time requirements. By improving the network structure of Yolo v3, the detection ability of small buildings is strengthened by modifying the resolution of feature map and adjusting the dimension of prior frame. The experimental results show that the improved Yolo v3 target detection method meets the real-time requirements and the detection accuracy and recall rate reaches 91.29% and 95.61%, which are 5.35% and 2.34% higher than the original algorithm. Therefore, the improved method effectively solves the detection problem of small buildings in remote sensing field.

Key words: target detection, remote sensing, deep learning, Yolo v3, image processing