Abstract: Aiming at the complex target imaging characteristics of remote sensing images, the traditional object detection and recognition technology has the problems of low accuracy and insufficient robustness. An on orbit object detection method is proposed in this paper based on deep learning for optical remote sensing image. At the hardware level, this method uses FPGA and multi core DSP to build an on board hardware processing platform, which can update the on orbit object library and optimize the performance of the deep learning model. At the algorithmic level, this method introduces deep separation convolution based on YOLOv3 feature extraction network(Darknet 53) to effectively compress model parameters and inference computation. In the detection stage, a local re detection module is added to improve the adaptability of the algorithm to dense targets. Compared with the current object detection methods, this method has a great improvement in processing speed and accuracy, the detection accuracy of the target is higher than 90%, and the unit processing speed reaches 334.24FPS. At the same time, it supports the detection of aircraft, ships, vehicles and other typical targets, laying a foundation for model application.

Key words: remote sensing image, target detection, deep learning, YOLOv3, FPGA