一个轻量级的水下探测器,增强了注意力机制,GSConv和YOLOv8上的WIoU
Shaobin Cai1, Xiangkui Zhang2, Yuchang Mo3
1College of Informaton Engineering, Huzhou University, Huzhou, 313000, China. caishaobin@zjhu.edu.cn.
Scientific reports
|October 29, 2024
概括
一个新的轻量级水下探测器,AGW-YOLOv8,通过提高图像质量和网络效率来提高目标检测准确性. 这种方法在较少的参数下实现了比YOLOv8.8更高的平均平均精度 (mAP).
科学领域:
- 计算机视觉 计算机视觉
- 海洋技术 海洋技术
- 人工智能的人工智能
背景情况:
- 水下目标检测对于环境和海洋监测至关重要.
- 在复杂的水下环境中图像质量差,妨碍了检测准确度.
- 水下设备的有限存储和计算能力限制了先进的检测技术.
研究的目的:
- 提出AGW-YOLOv8,一个基于YOLOv8.8的轻量级水下探测器.
- 通过提高图像质量和网络效率来提高水下目标检测性能.
- 为了解决现有的水下检测方法的局限性.
主要方法:
- 使用有限对比度适应性直方图平衡和波形变换 (LCAHE-WT) 来提高图像保真度和细节.
- 集成的CBAM (卷积块注意模块) 为有效的道特征提取,同时保留空间信息.
- 使用GSConv来减少模型参数和计算复杂度.
- 将SE注意力机制纳入C2f模块,以关注重要功能.
- 应用 WIoU (Wise-IoU) 用于动态梯度分布,以提高概括性和性能.
主要成果:
- 在URPC2020数据集上,AGW-YOLOv8实现了82.9%的平均精度 (mAP).
- 与标准YOLOv8.8相比,这表示mAP增加了2.5%.
- 拟议的模型具有2.95M参数,低于YOLOv8的3.01M参数,表明效率有所提高.
结论:
- 与YOLOv8.8相比,AGW-YOLOv8在水下目标检测方面表现出优异的性能.
- 图像增强技术和注意力机制的整合导致了更高效,更准确的探测器.
- 拟议的轻量级模型适用于资源有限的水下设备.
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