An improved YOLOv5s method of vehicle target detection in remote sensing images

Junmiao Liu; Haichen Wang

doi:10.1117/12.3040986

11 September 2024 An improved YOLOv5s method of vehicle target detection in remote sensing images

Junmiao Liu, Haichen Wang

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132530R (2024) https://doi.org/10.1117/12.3040986
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

In target detection, remote sensing images have characteristics such as complex background, dense target distribution, and small targets, which lead to poor detection results, missed detections and false detection. This paper presents ABSYOLOv5s, a target detection method based on YOLOv5s. In order to overcome the difficulties in detecting small target and prevent missed detection and error detection, a self-attention mechanism with position information encoding is introduced to strengthen the fusion of feature information within the same scale. BiFPN is used in the neck network to better integrate low-level and high-level feature information. In addition, the ShapeIoU loss function proposed by Zhang et al. is applied to make the model in this article concentrate more on bounding box’s shape and scale to improve detection accuracy. This paper conducted a complete experiment on the remote sensing vehicle data set COWC. The experiment shows that all indicators of the improved model have been improved, with the accuracy increased by 0.4%, recall, mAP,mAP@0.5/0.95, have improved by 1.1 per cent, 0.7 per cent, and 0.4 per cent, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junmiao Liu and Haichen Wang "An improved YOLOv5s method of vehicle target detection in remote sensing images", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132530R (11 September 2024); https://doi.org/10.1117/12.3040986

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KEYWORDS

Target detection

Remote sensing

Detection and tracking algorithms

Feature fusion

Object detection

Feature extraction

Performance modeling

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