基于YOLOv5s的遥感图像的车辆小目标检测

摘要/Abstract

摘要： 由于YOLOv5s检测效果好、计算复杂度低而被广泛应用于各类目标检测任务，但是其过大的下采样步长导致对卫星遥感图像中的车辆小目标检测难以获得满意的结果。为了提升对小目标检测的性能，基于YOLOv5s采取降低下采样步长的策略以保护车辆小目标的纹理和几何特性，同时在检测头前插入注意力机制模块以抑制复杂背景对目标的干扰。在0.5 m/pixel分辨率的自建数据集上进行测试，提出的SA-YOLOv5s对车辆目标检测的AP、Recall、Precision值分别达到90.1%、89%和 87.3%，与YOLOv5s相比分别提升了16.4、6和5个百分点，表现出良好的检测性能。

关键词: 遥感图像, 小目标检测, YOLOv5, 自主数据集

Abstract: Because of its good detection effect and low computational complexity， YOLOv5s is widely used in various target detection tasks. However， its large downsampling stride makes it difficult to obtain satisfactory results for small-sized vehicle detection in satellite remote sensing images. In order to improve the performance of small target detection， on the basis of YOLOv5s， the strategy of reducing the downsampling stride is adopted to protect the texture and geometric features of small targets in the vehicle， and the attention mechanism module is inserted in front of the detection head to suppress the interference of complex background. The tested results on the autonomous data set with a resolution of 0.5 m/pixel show that the AP， recall and precision of SA-YOLOv5s for vehicle target detection reached 94.1%， 99% and 87.3% respectively， which were 16.4， 6 and 5 percentage points higher than YOLOv5s， and showed good detection performance.

Key words: remote sensing image, small object detection, YOLOv5, autonomous dataset

邱地发, 于淑芳, 刘锦辉, 毕梦昭. 基于YOLOv5s的遥感图像的车辆小目标检测[J]. 计算机与现代化, 2023, 0(05): 122-126.

QIU Di-fa, YU Shu-fang, LIU Jin-hui, BI Meng-zhao. Improved YOLOv5s for Small Vehicle Object Detection on Remote Sensing Image[J]. Computer and Modernization, 2023, 0(05): 122-126.

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