CCW-YOLOv5:基于坐标卷积和修改边界损失的前性声纳目标方法
1College of Information Science and Engineering, Ocean University of China, Qingdao, China.
PloS one
|June 3, 2024
概括
这项研究增强了YOLOv5用于多束前声纳 (MFLS) 目标检测. 改进的CCW-YOLOv5算法在复杂的水下环境中提高了准确性和速度.
科学领域:
- 海洋技术 海洋技术
- 人工智能的人工智能是人工智能.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 多束前声纳 (MFLS) 对于水下探测至关重要.
- 复杂的环境和噪音限制MFLS识别性能.
- 现有的MFLS系统在不清楚的特征和稀缺数据方面扎.
研究的目的:
- 改进YOLOv5以使用MFLS数据进行增强的水下目标检测.
- 为了应对有限的声纳数据和特征提取的挑战.
- 提高MFLS目标识别的准确性和通用性.
主要方法:
- 适应YOLOv5架构,具有转移学习,以处理有限的声纳图像数据.
- 集成的坐标卷积来增强对小目标的位置特征的提取.
- 整合了注意力机制,以优化特征学习和扩大受体场.
- 修改了界限框损失函数,以减轻训练样本质量不均的问题.
主要成果:
- 拟议的CCW-YOLOv5算法实现了对象检测的85.3%mAP@0.5.
- 在本地机器上展示了54 FPS的最快推断速度.
- 与现有算法相比,在检测准确度和性能方面取得了显著的改进.
结论:
- CCW-YOLOv5算法为使用MFLS的水下目标检测提供了卓越的性能.
- 这些改进提高了模型在各种水下条件中的适用性和通用性.
- 这项研究为具有挑战性的MFLS目标识别任务提供了强大的解决方案.
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