Improved YOLOv5s for Small Vehicle Object Detection on Remote Sensing Image

Abstract

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

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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