用多光谱成像+AI控制实现废料自动分拣，提升回收效率。
First Lessons Learned of an Artificial Intelligence Robotic System for Autonomous Coarse Waste Recycling Using Multispectral Imaging-Based Methods
- 融合紫外到短波红外多光谱图像进行材料分类
- 在损毁物占比高的废料堆中实现90%以上分类准确率
- 适合工业自动化回收场景，尤其适用于重型机械无人操作
当前粗粒度废物处理设施依赖人工结合大型机械进行物料分拣，大量可回收物被混入粗废料中浪费。为提高回收率，亟需开发更高效的分拣技术。核心挑战在于：如何在混合废料堆中实现材料的物体检测与分类，以及如何实现液压机械设备的自主控制。由于多数废弃物已破损或破坏，单纯依赖物体检测难以奏效。为此，本文提出基于紫外（UV）、可见光（VIS）、近红外（NIR）及短波红外（SWIR）多光谱成像的材料分类方法。同时，探索使用低成本摄像头与基于人工智能的控制器，实现大型废料分拣设备的自主操控，推动智能回收系统落地。
原文摘要 · Abstract (English)
Current disposal facilities for coarse-grained waste perform manual sorting of materials with heavy machinery. Large quantities of recyclable materials are lost to coarse waste, so more effective sorting processes must be developed to recover them. Two key aspects to automate the sorting process are object detection with material classification in mixed piles of waste, and autonomous control of hydraulic machinery. Because most objects in those accumulations of waste are damaged or destroyed, object detection alone is not feasible in the majority of cases. To address these challenges, we propose a classification of materials with multispectral images of ultraviolet (UV), visual (VIS), near infrared (NIR), and short-wave infrared (SWIR) spectrums. Solution for autonomous control of hydraulic heavy machines for sorting of bulky waste is being investigated using cost-effective cameras and artificial intelligence-based controllers.
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