CER-DETR:在复杂的天气条件下对浮动碎片进行通道感知和边缘调整的检测
Hong Shao1, Shuo Wang1, Wencheng Cui1
1Shenyang University of Technology, 111 Shenliao West Road, Shenyang, 110870, China.
Marine pollution bulletin
|December 10, 2025
概括
这项研究介绍了CER-DETR,这是一种轻量级的人工智能框架,用于在恶劣天气中检测漂浮碎片. 它提高了重要的水环境监测的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 环境监测 环境监测
- 人工智能的人工智能
背景情况:
- 精确检测漂浮碎片对于水资源管理和生态保护至关重要.
- 由于不可预测的天气条件影响图像质量,现有的方法难以通用.
- 开发针对各种环境条件的强大的检测系统仍然是一个重大挑战.
研究的目的:
- 提出一个轻量级,单阶段检测框架 (CER-DETR),用于在复杂的气象条件下适应性检测碎片.
- 为了提高小物体的检测,并抑制退化图像的背景噪声.
- 为了提高准确性和效率的权衡,用于现实世界水面碎片的监测.
主要方法:
- 开发了一个通道意识轻量级边缘对齐的残余门 DETR (CER-DETR) 框架.
- 关键的创新包括轻量级通道感知残留区块 (LCABlock) 以提高信号噪声比,以及门式残留注意区块 (GRAB) 以抑制噪声.
- 尺度对齐边缘融合 (SAEF) 用于适应性跨尺度融合.
主要成果:
- 在只有1180万个参数的流量数据集上,CER-DETR实现了87.23%的50%重叠 (mAP50) 的平均平均精度.
- 在复杂的天气子集中记录了高mAP50分数:85.13% (低光),87.53% (雨),85.39% (雾),和85.99% (混合).
- 与主流方法相比,该方法在各种数据集和条件下表现出更高的性能和效率.
结论:
- 拟议的CER-DETR框架有效地处理复杂的天气造成的成像退化,在准确性和效率方面提供了显著的优势.
- 这种方法为漂浮碎片的检测提供了强大的解决方案,有助于改善水环境监测和保护工作.
- 轻量级的设计使其适合在实时水质管理系统中实际部署.
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