水二分网 (WaterBiSeg-Net):一个用于海洋垃圾细分的水下双边细分网络
Wenming Zhang1, Bofeng Wei1, Yaqian Li1
1Key Lab of Industrial Computer Control Engineering of Heibei Province, Yanshan University, Qinhuangdao 066004, China.
Marine pollution bulletin
|July 3, 2024
概括
一个新的实时语义细分网络,WaterBiSeg-Net,有效地识别了自动水下车辆 (AUV) 的海洋垃圾. 这个解决方案克服了模糊图像和背景干扰的挑战,提供准确,低成本的海洋碎片检测.
科学领域:
- 海洋环境保护 海洋环境保护
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 海洋垃圾对海洋生态系统构成重大威胁.
- 装备有视觉识别的自动水下车辆 (AUV) 对海洋垃圾清理至关重要.
- 目前的识别算法存在速度缓慢,计算成本高,容易出现图像质量问题.
研究的目的:
- 开发一个实时语义细分网络,通过AUV准确识别海洋垃圾.
- 解决现有算法的局限性,包括缓慢的推断和易受图像退化的影响.
- 为海洋垃圾细分提供计算效率高的解决方案.
主要方法:
- 提出了WaterBiSeg-Net,一个实时语义细分网络.
- 引入了多尺度信息增强模块,以减轻低清和模糊图像的影响.
- 开发了封闭聚合层,以减少背景干扰.
- 实施了一种用于直接提取边界信息的新方法.
主要成果:
- 在SUIM和TrashCan数据集上,WaterBiSeg-Net在海洋垃圾细分方面表现出卓越的表现.
- 该网络提供准确的细分结果,对于实时AUV操作至关重要.
- 在具有挑战性的水下条件下实现了有效和准确的海洋垃圾识别.
结论:
- WaterBiSeg-Net为AUV提供了低计算成本和实时解决方案,用于识别海洋垃圾.
- 拟议的网络有效地处理模糊图像和背景噪声,提高分段精度.
- 这项研究提升了AUV在海洋环境保护工作中的能力.
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