Remote Sensing Image Object Detection Based on Bidirectional Feature Fusion and Feature Selection

In remote sensing image object detection, the complex background always occupies a large area of the entire image, which seriously affects the object detection effect. This paper proposes an object detection network that can perform multiple feature fusion and selection on feature maps. A feature fusion network is used to fuse deep and shallow features to improve the detection effect of small objects in complex background. While retaining the up-bottom path of the feature fusion network, it adds a bottom-up path to diminish the number of network layers that the shallow features need to pass on to the top layer, thereby reducing the loss of shallow features. In order to reduce the interference of useless information in the fusion feature maps with detection network, a multiple feature selection module is designed. The attention mechanism in the multiple feature selection module enables the network to adaptively focus on more important features, ignore useless features. Since the conventional five-parameter regression method has serious boundary problems, the angle prediction is often inaccurate for objects with a large aspect ratio, to solve this problem, the proposed method treats angle prediction as a classification task. The mAP of our method on DOTA and self-made dataset DOTA-GF reaches 0.651 and 0.641, and the comparative experiments with mainstream object detection methods demonstrate the effectiveness of the proposed method. © 2022, Chinese Institute of Electronics. All right reserved.