Improved YOLOv5's remote sensing image detection algorithm

Naikang Chen; Jinmin Zhang

doi:10.1117/12.3033719

13 June 2024 Improved YOLOv5's remote sensing image detection algorithm

Naikang Chen, Jinmin Zhang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800A (2024) https://doi.org/10.1117/12.3033719
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Addressing the challenges inherent in remote sensing image detection, notably the YOLOv5 detector's subpar accuracy due to limited detection target features, intricate detection backgrounds, and predominantly small targets, this paper presents the RS-YOLOv5 algorithm, an enhancement of the YOLOv5s model. Primarily, the conventional convolution in the backbone network is substituted with full-dimensional dynamic convolution to enrich the information gathered across multiple dimensions, thereby enhancing the extraction of target features. Additionally, a decoupled detection head replaces the traditional detection head, refining classification and positioning tasks while strategically leveraging features from different levels to mitigate background interference on small target detection. Next, adding the Normalized Wasserstein distance (NWD) measurement to the loss function is intended to enhance the accuracy of small target detection. The ablation analysis conducted on the DOTA dataset showcases a noteworthy improvement in the average accuracy of object detection in remote sensing imagery facilitated by the proposed RS-YOLOv5 algorithm, culminating in a 2.5% increase in average accuracy compared to the original algorithm.

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

Citation Download Citation

Naikang Chen and Jinmin Zhang "Improved YOLOv5's remote sensing image detection algorithm", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800A (13 June 2024); https://doi.org/10.1117/12.3033719

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KEYWORDS

Object detection

Detection and tracking algorithms

Convolution

Target detection

Remote sensing

Small targets

Feature extraction

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