A Lightweight and Efficient Network for Maritime Object Detection
A Lightweight and Efficient Network for Maritime Object Detection
- New search for: Chen, Gang
- New search for: Sun, Shiping
- New search for: Li, Feng
- New search for: Duan, Jiaxing
- New search for: Zhang, Xiaogang
- New search for: Chen, Gang
- New search for: Sun, Shiping
- New search for: Li, Feng
- New search for: Duan, Jiaxing
- New search for: Zhang, Xiaogang
Maritime object detection is a crucial task in the environmental perception of unmanned surface vehicles. It faces challenges such as complex backgrounds, varying object scales, and numerous small objects. To address these issues, we propose a lightweight network based on YOLOv7-tiny. Our proposed method introduces the P2 detection head with high resolution to improve small object detection, the parameter-free attention module SimAM for better object feature extraction in complex maritime backgrounds, and the CARAFE module to reduce the loss of feature information during upsampling. We conducted experiments on a self-made maritime object detection dataset, and our proposed model outperforms the original YOLOv7-tiny by 2.6% mAP@.5:.95, achieving a real-time inference speed of 101 FPS. Furthermore, our proposed model performs better than other lightweight models.
Document information
A Lightweight and Efficient Network for Maritime Object Detection
2023 China Automation Congress (CAC) ; 3393-3398
2023-11-17
1079831 byte
Conference paper
Electronic Resource
English
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