Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing Images

Abstract

Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There are two main reasons for the above problems, one is the mismatch of the receptive fields, and the other is the lack of feature information. For resolving the problem that multi-scale ship targets are difficult to detect, this paper proposes a ship target detection algorithm based on feature enhancement. Firstly, EIRM (Elastic Inception Residual Module) is proposed for feature enhancement, which can capture feature information of different dimensions and provide receptive fields of different scales for mid- and low-level feature maps. Secondly, the SandGlass-L block is proposed by replacing the ReLu6 activation function of the SandGlass block with the Leaky ReLu activation function. Leaky ReLu solves the problem of 0 output when ReLu6 has negative input, so the SandGlass-L block can retain more feature information. Finally, based on SandGlass-L, SGLPANet (SandGlass-L Path Aggregation Network) is proposed to alleviate the problem of information loss caused by dimension transformation and retain more feature information. The backbone network of the algorithm in this paper is CSPDarknet53, and the SPP module and EIRM act after the backbone network. The neck network is SGLPANet. Experiments on the NWPU VHR-10 dataset show that the algorithm in this paper can well solve the problem of low detection accuracy caused by mismatched receptive fields and missing feature information. It not only improves the accuracy of ship target detection, but also achieves good results when extended to other categories. At the same time, the extended experiments on the LEVIR dataset show that the algorithm also has certain applicability on different datasets.