通过边界引导抓取检测,提高建筑和拆除废弃物流中的分类效率
Vineet Prasad1, Mehrdad Arashpour1
1Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
Waste management (New York, N.Y.)
|September 17, 2025
概括
这项研究引入了在建筑和拆除废物 (CDW) 回收中用于机器人掌握检测的新方法,提高了混乱环境中的准确性. 它还介绍了ReCoDeWaste,这是首个用于CDW实例细分和掌握检测的数据集.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 废物管理和回收利用
- 计算机视觉 计算机视觉
背景情况:
- 机器人自动化对于建筑和拆除废弃物 (CDW) 价值化至关重要,但对回收利用的掌握检测尚未得到充分探索.
- 当前人工智能驱动的计算机视觉 (CV) 在CDW识别方面表现出色,但在以行动为中心的任务中扎,例如在杂乱的场景中抓取检测.
- 一个显著的局限性是,用于训练机器人系统的掌握注释数据的稀缺性.
研究的目的:
- 开发一种强大的方法,用于CDW中的机器人抓取检测.
- 为了应对确定CDW可回收材料的最佳,无碰撞的抓柄姿势的挑战.
- 引入一个新的数据集用于培训和评估CDW实例细分和掌握检测模型.
主要方法:
- 建议使用注意力特征融合的边界引导抓取检测模型.
- 该方法利用了形状感知CDW实例细分和LLM启用自动标签方面的进展.
- ReCoDeWaste数据集,在杂乱的场景中包含超过10万个注释实例,用于RGB-D CDW实例细分和抓取检测.
主要成果:
- 边界导向模型成功地预测了杂乱的CDW流中的无碰撞抓取.
- 这种方法在标准评估指标方面表现优于最先进的方法.
- 达到了高达94.36%的掌握检测准确度,证明了该模型的有效性.
结论:
- 这项研究通过推进以行动为重点的CV任务,而不仅仅是简单的认可,从而提高了CDW的价值.
- 开发的方法和数据集有助于更高效,更准确的机器人对建筑和拆除废物的分类.
- 未来的工作可以在这些发现的基础上进一步自动化废物回收过程.
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