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多式融合图像增强技术和CFEC-YOLOv7用于水下目标检测算法研究
1School of Internet of Things Engineering, Wuxi Institute of Technology, Wuxi, China.
Frontiers in neurorobotics
|July 4, 2025
概括
本研究介绍了一种先进的水下图像增强技术,以提高目标识别. 拟议的方法有效地减少了模糊,并提高了水下检测系统的准确性.
科学领域:
- 海洋技术 海洋技术
- 计算机视觉 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 由于复杂的条件,水下环境对图像清晰度提出了重大挑战.
- 在水下图像中,严重的静态和动态模糊阻碍了准确的目标识别和检测.
研究的目的:
- 开发一种强大的方法来增强水下图像,以提高目标识别精度.
- 解决水下图像中的静态和动态模糊问题.
主要方法:
- 适应性色彩补偿和改进的多尺度视网膜 (MSR) 对于静态模糊.
- 恢复器网络用于动态移除模糊 (摄像机震动,失焦,运动).
- 拟议的CFEC-YOLOv7网络具有CCBC模块,ECLOU损失和用于目标识别的FasterNet.
主要成果:
- 拟议的水下图像增强方法显著减少模糊.
- 定性和定量分析证实了增强技术的有效性.
- CFEC-YOLOv7模型在水下目标识别任务中表现出色.
结论:
- 开发的水下图像增强和目标识别方法对于复杂的水下环境是有效的.
- CFEC-YOLOv7模型显示出强大的适应性和潜能,可用于水下探测的实际应用.
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