基于YOLOv5s的轻量化遥感舰船检测算法

王浩臣; 辛月兰; 郭江; 王庆庆

doi:10.19693/j.issn.1673-3185.03454

基于YOLOv5s的轻量化遥感舰船检测算法

Lightweight remote sensing ship detection algorithm based on YOLOv5s

摘要

摘要:
目的针对遥感图像舰船目标检测任务中轻量化和快速推理的需求，提出了一种基于改进YOLOv5s的轻量化遥感舰船目标检测算法LR-YOLO。
方法首先，主干网络采用ShuffleNet v2 Block堆叠方式，有效减少网络模型的参数量并提高计算速度；其次，设计区域选择模块Filter，选择感兴趣的区域，更充分地提取有效特征；最后，引入圆形光滑标签计算角度损失，对遥感舰船目标进行旋转检测，并采用可变形卷积，以此来适应几何形变，提升检测效果。
结果在HRSC2016舰船数据集上的实验结果表明，该算法的检测精度达到了92.90%，提高了1.3%，并且模型参数量仅为基线模型的39.33%。
结论该算法实现了轻量化和检测准确率的平衡，为轻量化遥感舰船目标检测提供了参考。

Abstract:
Objectives A lightweight remote sensing ship target detection algorithm LR-YOLO based on improved YOLOv5s is proposed to meet the requirements of lightweight and fast inference in ship target detection tasks in remote sensing images.
Methods Firstly, the backbone network adopts ShuffleNet v2 Block stacking method, effectively reducing the number of network model parameters and improving computational speed; Secondly, design a region selection module Filter to select regions of interest and extract effective features more fully; Finally, a circular smooth label is introduced to calculate angle loss, perform rotation detection on remote sensing ship targets, and use deformable convolution to adapt to geometric deformation and improve detection performance.
Results Experimental results on the HRSC2016 ship dataset show that the detection accuracy of the algorithm reaches 92.90%, which is improved by 1.3%, and the number of model parameters is only 39.33% of that of the baseline model.
Conclusions The algorithm achieves the balance between lightweight and detection accuracy, and provides a reference for remote sensing ship target detection.

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