A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images

Object detection in remote sensing images has been widely used in military and civilian fields and is a challenging task due to the complex background, large-scale variation, and dense arrangement in arbitrary orientations of objects. In addition, existing object detection methods rely on the increasingly deeper network, which increases a lot of computational overhead and parameters, and is unfavorable to deployment on the edge devices. In this paper, we proposed a lightweight keypoint-based oriented object detector for remote sensing images. First, we propose a semantic transfer block (STB) when merging shallow and deep features, which reduces noise and restores the semantic information. Then, the proposed adaptive Gaussian kernel (AGK) is adapted to objects of different scales, and further improves detection performance. Finally, we propose the distillation loss associated with object detection to obtain a lightweight student network. Experiments on the HRSC2016 and UCAS-AOD datasets show that the proposed method adapts to different scale objects, obtains accurate bounding boxes, and reduces the influence of complex backgrounds. The comparison with mainstream methods proves that our method has comparable performance under lightweight.

Automated summary

A lightweight keypoint-based oriented object detector for remote sensing images is proposed in this paper, which improves detection performance by introducing a semantic transfer block and an adaptive Gaussian kernel, and obtains a lightweight student network using distillation loss associated with object detection. Experimental results show that the method adapts to objects of different scales, obtains accurate bounding boxes, and reduces the influence of complex backgrounds. The comparison with mainstream methods demonstrates comparable performance under lightweight conditions.