At present, remote sensing image vehicle detection based on deep learning has achieved certain results, but most of them rely on powerful PC computing power and cannot be deployed in satellites, so they cannot provide support for satellite in-orbit detection. Aiming at this problem, this paper proposes a remote sensing image vehicle detection method based on YOLOv5 model and successfully deploys it in Jetson TX2 embedded equipment that can be deployed on a satellite platform. Experiments have proved that the algorithm proposed in this article detects vehicle targets in a 12000*12000 pixels wide remote sensing image in an embedded device, and the detection time is only about 1 minute and 20 seconds at the fastest.
Remote sensing image object detection is the primary task in the field of intelligent processing and has important practical application value. However, the current intelligent processing method of remote sensing images is difficult to meet the real-time requirements. In order to improve the effectiveness, real-time on-board processing of the collected images has become an important direction. This article compares several commonly used deep learning object detection algorithms, selects YOLO v3 for in-depth research and optimization, and transplants the debugged algorithm to the Cambrian 1H8 embedded edge intelligent computing platform for performance testing. Experiments show that the algorithm has high accuracy and the running speed basically meets the real-time requirements. It can be used to study and test the real-time processing performance of remote sensing images.
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