关键词: 航空影像, 目标检测, 图像融合, 特征金字塔, 区域建议网络

Abstract:

Aiming at the shortcomings of the general target detection framework for insufficient target detection performance in aerial imagery, an improved Mask R-CNN algorithm is proposed for target detection in aerial imagery. The algorithm adds an image fusion network to fuse visible light and infrared images to eliminate the impact of shadowing on the target. At the same time, the feature pyramid structure is improved, so that the high-level semantic features and low-level localization information in the feature extraction process are fully integrated, and the detection accuracy of targets at various scales is improved. In order to solve the problems of low detection accuracy and high localization difficulty of small targets, the algorithm uses a new region proposal network SD-RPN, and sets a reasonable size sliding window on convolutional layers of different depths to detect targets of different scale types, which makes the proposed region more accurate. Experimental results show that the proposed algorithm performs well on the VEDAI dataset in comparison with current mainstream detection algorithms, and the detection accuracy is greatly improved, especially for small target detection.

Key words: aerial image, target detection, image fusion, feature pyramid, region proposal network