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- Title
AMEA-YOLO: a lightweight remote sensing vehicle detection algorithm based on attention mechanism and efficient architecture.
- Authors
Wang, Shou-Bin; Gao, Zi-Meng; Jin, Deng-Hui; Gong, Shu-Ming; Peng, Gui-Li; Yang, Zi-Jian
- Abstract
Due to the large computational requirements of object detection algorithms, high-resolution remote sensing vehicle detection always involves numerous small objects, high level of background complexity, and challenges in balancing model accuracy and parameter count. The attention mechanism and efficient architecture lightweight-YOLO (AMEA-YOLO) is proposed in this paper. A lightweight network as the backbone network of AMEA-YOLO is designed, and it could maintain model accuracy and ensure good lightweight. FasterNet is employed to accelerate model training speed. The enhanced deep second-order channel attention module (EnhancedSOCA) is utilized to improve the image high-resolution. In addition, a lightweight module is devised to further reduce the model's weight. The implementation of the HardSwish activation function improves model accuracy. The experimental results indicate that the AMEA-YOLO algorithm could ensure model lightweight and accurate performance.
- Subjects
REMOTE sensing; OBJECT recognition (Computer vision); ALGORITHMS; INTELLIGENT transportation systems
- Publication
Journal of Supercomputing, 2024, Vol 80, Issue 8, p11241
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-023-05872-2