从高度混乱的建筑和拆除废物流中实时细分可回收材料的实例
Vineet Prasad1, Mehrdad Arashpour1
1Department of Civil Engineering, Monash University, Melbourne, Australia.
Journal of environmental management
|November 24, 2024
概括
深度学习在混乱的环境中难以识别建筑和拆除废物 (CDW) 的可回收物. 对于自动化废物分类,新方法可将定位精度提高12.9%,并将预测精度提高68%.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 建筑和拆除废物 (CDW) 的扩大需要有效的回收策略.
- 使用深度学习的CDW可回收材料的自动识别是有希望的,但由于混乱和复杂性而受到挑战.
- 精确和快速定位对于机器人垃圾分类至关重要.
研究的目的:
- 在复杂的CDW流中全面评估可回收材料的最新实时实例细分.
- 解决当前深度学习模型在处理CDW复杂性的局限性,如变形,污染和杂乱.
- 提出和评估新的技术,以提高CDW可回收本地化的准确性和效率.
主要方法:
- 策划并使用高质量的CDW实例细分数据集,捕捉现实世界的复杂性.
- 评估了最先进的深度学习网络,用于实时实例细分.
- 基于补丁的综合推断技术,以加强对杂乱地区的关注.
- 开发了一个域转移框架,以提高基于提示的细分的零射击能力.
主要成果:
- 先进的网络在复杂的CDW流中实现了不到50%的细分精度,原因是混乱度很高.
- 基于补丁的推断提高了12.9%的整体性能.
- 拟议的域名转移框架增加了68%的正确口罩预测,用于零射击识别.
结论:
- 目前的深度学习模型在从复杂的CDW中细分可回收材料方面面临重大挑战.
- 基于补丁的推断和域转移框架为增强自动化CDW回收提供了实际解决方案.
- 这项研究为环境管理中应用深度学习提供了一个参考,并建议废物分类的最佳建筑框架.
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