Abstract: Most existing methods have low recognition accuracy for fine-grained remote sensing vessel detection,and the large computation and storage requirements associated with the commonly used floating-point precision data types make it difficult to meet the needs of model in-orbit deployment due to the limited power of on-board devices.To address these challenges,this paper proposed a fast target detection method for fine-grained remote sensing vessels based on model quantization.Firstly,a fusion intelligence-based detection network was designed to solve the problem of“large intra-class differences and small inter-class differences”,which can effectively improve the accuracy of fine-grained vessel detection and identification.On this basis,a high-precision model quantization method was proposed to optimize the clipping boundary,which could effectively improve the inference speed.Experimental test results show that the proposed method achieves a maximum accuracy improvement of more than 5.9% compared with existing studies,while the quantization method can achieve a maximum performance improvement of 1.2%.It can effectively reduce the calculation load while maintaining a high accuracy,thus can be easily applied to satellite-based computing units.

Key words: satellite remote sensing vessel detection, fast target detection, CNN model quantification, satellite applications, deep neural networks