Effectively disassembling and recovering materials from waste electrical and electronic equipment (WEEE) is a critical step in moving global supply chains from carbon-intensive, mined materials to recycled and renewable ones. Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery. Many WEEE objects share many key features and therefore can look quite similar, but their material composition and internal component layout can vary, and thus it is critical to have an accurate classifier for subsequent disassembly steps for accurate material separation and recovery. This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. More specifically, this work develops Iterative Class Activation Mapping (iCAM), a novel network architecture that explicitly focuses on the finer-details in the multi-modal feature maps that are needed for accurate electronic object classification. In order to train a classifier, electronic objects lack large and well annotated X-ray datasets due to expense and need of expert guidance. To overcome this issue, we present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98.6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89.1% (RGB) and 97.9% (X-ray) independently. We provide experimental results3 to corroborate our results.
翻译:从废弃电气和电子设备(WEEEE)中有效拆卸和回收材料,有效地拆卸废弃电气和电子设备(WEEEE)是推动全球供应链从碳密集、开采材料转向回收和可再生材料的关键步骤。常规回收流程依靠粉碎和分类废物流,但对于由许多不同材料组成的WEEEEE,我们探索有针对性地拆卸大量物体,以改进材料回收。许多WEEEEE物体具有许多关键特征,因此可以看起来非常相似,但其材料构成和内部组成部分布局可能不同,因此,必须有一个准确的分类器,以便随后采取拆分步骤,实现准确的材料分离和回收。这项工作引入了RGB-X,即多模式图像分类法,采用外部RGB-X图像的关键特征,用X光谱图像生成的图像对电子物品进行准确分类。更大规模和精确的 RGB-R-RR-RRRR 版本数据映射数据映射(i CAM)是一个新型的网络结构结构,它明确侧重于电子物体分类的结果。为了训练一个精精密的精密的精密的 RGB-x-x-xxxxlalal-roalalalalalalalal roal 版域 需要一个更大规模的X-rocreal 10 arocreal arocreal rodu化的X)