Remote Sensing Ship Detection and Classification Based on Improved BBAVectors Algorithm

Abstract

In recent years, most of remote sensing object detection algorithms only pursue higher detection accuracy, which use two-stage anchor-based networks. However, the two-stage anchor-based network usually has many parameters, complex calculation, and low detection speed, which is not suitable for object detection tasks with real-time requirements. BBAVectors is a network based on the one-stage anchor-free type CenterNet architecture. It has a fast detection speed, but the detection accuracy is far lower than the current high-precision remote sensing object detectors. In order to improve the detection accuracy of BBAVectors so that it can be better used in real-time remote sensing ship detection and classification, we propose the BBAV-ACPP. Based on the BBAVectors architecture, we add the Coordinate Attention Module and modify the center point representation. The proposed BBAV-ACPP architecture is trained and tested on the public remote sensing ship dataset HRSC 2016. Experiments show that the proposed BBAV-ACPP architecture can improve the detection accuracy while maintaining a fast detection speed. In some remote sensing ship detection tasks with real-time requirements, BBAV-ACPP can detect ship targets quickly and accurately.