Real-Time Processing of Ship Detection with SAR Image Based on FPGA

With the development of Synthetic Aperture Radar (SAR), there is a growing demand for rapid SAR image processing. However, traditional Graphics Processing Unit (GPU) based processing faces challenges in meeting real-time application requirements, especially in scenarios like maritime search and rescue, due to time delays caused by input/output data transmission through the Peripheral Component Interconnect Express (PCIe) bus and high power consumption. To address this issue, our research proposes a real-time SAR ship detection system based on a lightweight Field-Programmable Gate Array (FPGA). The system utilizes a trainable pseudo-color synthesis network for SAR image preprocessing and employs a generic convolutional architecture to convert the YOLO v5 model into FPGA-executable hardware language. Experimental results indicate that the FPGA achieves a processing time of 68.9 milliseconds, significantly outperforming the GPU (234.7 milliseconds), without compromising detection accuracy. This research enhances SAR image detection speed, with implications for deploying object detection. algorithms on FPGAs.