Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2022 Apr 28;22(9):3370. doi: 10.3390/s22093370.
The real-time performance of ship detection is an important index in the marine remote sensing detection task. Due to the computing resources on the satellite being limited by the solar array size and the radiation-resistant electronic components, information extraction tasks are usually implemented after the image is transmitted to the ground. However, in recent years, the one-stage based target detector such as the You Only Look Once Version 5 (YOLOv5) deep learning framework shows powerful performance while being lightweight, and it provides an implementation scheme for on-orbit reasoning to shorten the time delay of ship detention. Optimizing the lightweight model has important research significance for SAR image onboard processing. In this paper, we studied the fusion problem of two lightweight models which are the Coordinate Attention (CA) mechanism module and the YOLOv5 detector. We propose a novel lightweight end-to-end object detection framework fused with a CA module in the backbone of a suitable position: YOLO Coordinate Attention SAR Ship (YOLO-CASS), for the SAR ship target detection task. The experimental results on the SSDD synthetic aperture radar (SAR) remote sensing imagery indicate that our method shows significant gains in both efficiency and performance, and it has the potential to be developed into onboard processing in the SAR satellite platform. The techniques we explored provide a solution to improve the performance of the lightweight deep learning-based object detection framework.
船舶检测的实时性能是海洋遥感检测任务中的一个重要指标。由于卫星上的计算资源受到太阳能电池板大小和抗辐射电子元件的限制，因此信息提取任务通常在图像传输到地面后才执行。然而，近年来，基于单阶段的目标检测方法，如 You Only Look Once Version 5（YOLOv5）深度学习框架，在轻量级的同时表现出强大的性能，为在轨推理提供了实现方案，以缩短船舶滞留的时间延迟。优化轻量级模型对于 SAR 图像星上处理具有重要的研究意义。在本文中，我们研究了两个轻量级模型的融合问题，即坐标注意力（CA）机制模块和 YOLOv5 检测器。我们提出了一种新颖的轻量级端到端目标检测框架，该框架在适当位置的骨干中融合了 CA 模块：YOLO 坐标注意力 SAR 船舶（YOLO-CASS），用于 SAR 船舶目标检测任务。在 SSDD 合成孔径雷达（SAR）遥感图像上的实验结果表明，我们的方法在效率和性能方面都有显著提高，并且有可能在 SAR 卫星平台上发展为星上处理。我们探索的技术为改进基于轻量级深度学习的目标检测框架的性能提供了一种解决方案。
