Oriented Object Detection Based on Foreground Feature Enhancement in Remote Sensing Images

Abstract

Oriented object detection is a fundamental and challenging task in remote sensing image analysis and has received much attention in recent years. Optical remote sensing images often have more complex background information than natural images, and the number of annotated samples varies in different categories. To enhance the difference between foreground and background, current one-stage object detection algorithms attempt to exploit focus loss to balance the foreground and background weights, thus making the network more focused on the foreground part. However, the current one-stage object detectors still face two main challenges: (1) the detection network pays little attention to the foreground and does not make full use of the foreground information; (2) the distinction of similar object categories has not attracted attention. To address the above challenges, this paper presents a foreground feature enhancement method applied to one-stage object detection. The proposed method mainly includes two important components: keypoint attention module (KAM) and prototype contrastive learning module (PCLM). The KAM is used to enhance the features of the foreground part of the image and reduce the features of the background part of the image, and the PCLM is utilized to enhance the discrimination of samples between foreground categories and reduce the confusion of samples between different categories. Furthermore, the proposed method designs and adopts an equalized modulation focal loss (EMFL) to optimize the training process of the model and increase the loss weight of the foreground later in the model training. Experimental results on the publicly available DOTA datasets and HRSC2016 datasets show that our method exhibits state-of-the-art performance.