关键词: 目标检测, 遥感图像, 深度可分离卷积, 注意力机制, Anchor-Free机制, CenterNet算法

Abstract:

Predictive image detection based on deep learning has a wide range of applications in the fields of earth resource investigation, military reconnaissance, and environmental monitoring. More accurate and efficient target detection algorithms are the hotspots and difficulties of existing image detection research. An improved CenterNet algorithm is proposed to remote sensing image detection, which preprocesses the remote sensing image to adapt to the CenterNet network and improves the detection efficiency of the remote sensing image. The original network is improved, the standard convolution in the residual module is replaced by the depthwise separable convolutions, which effectively reduces the amount of network calculations and reduces redundancy. At the same time, an attention mechanism is added to suppress useless information and improves the accuracy of network detection. In view of the large observation area of ??the remote sensing image, the relatively small target, the large difference in target size and the uneven distribution, the false detection rate and the missed detection rate of the target are reduced. The experimental results show that the improved CenterNet algorithm has a significant improvement over the original CenterNet algorithm, which proves the robustness of the improved algorithm.

Key words: target detection, remote sensing images, depthwise separable convolutions, attention mechanism, Anchor-Free mechanism, CenterNet algorithm