Environmental pollution is a critical global issue, with recycling emerging as one of the most viable solutions. This study focuses on waste segregation, a crucial step in recycling processes to obtain raw material. Recent advancements in computer vision have significantly contributed to waste classification and recognition. In waste segregation, segmentation masks are essential for robots to accurately localize and pick objects from conveyor belts. The complexity of real-world waste environments, characterized by deformed items without specific patterns and overlapping objects, further complicates waste segmentation tasks. This paper proposes an Ensemble Learning approach to improve segmentation accuracy by combining high performing segmentation models, U-Net and FPN, using a weighted average method. U-Net excels in capturing fine details and boundaries in segmentation tasks, while FPN effectively handles scale variation and context in complex environments, and their combined masks result in more precise predictions. The dataset used closely mimics real-life waste scenarios, and preprocessing techniques were applied to enhance feature learning for deep learning segmentation models. The ensemble model, referred to as EL-4, achieved an IoU value of 0.8306, an improvement over U-Net's 0.8065, and reduced Dice loss to 0.09019 from FPN's 0.1183. This study could contribute to the efficiency of waste sorting at Material Recovery Facility, facilitating better raw material acquisition for recycling with minimal human intervention and enhancing the overall throughput.

翻译：环境污染是一个严峻的全球性问题，而回收利用已成为最可行的解决方案之一。本研究聚焦于废弃物分拣，这是回收过程中获取原材料的关键步骤。计算机视觉领域的最新进展显著推动了废弃物的分类与识别。在废弃物分拣中，分割掩码对于机器人准确定位并从传送带上拾取物体至关重要。现实世界废弃物环境的复杂性，表现为物品变形无固定模式以及物体相互重叠，进一步加大了废弃物分割任务的难度。本文提出一种集成学习方法，通过加权平均法结合高性能分割模型U-Net与FPN，以提高分割精度。U-Net擅长捕捉分割任务中的精细细节与边界，而FPN能有效处理复杂环境中的尺度变化与上下文信息，二者掩码的结合可产生更精确的预测。所用数据集高度模拟真实废弃物场景，并应用了预处理技术以增强深度学习分割模型的特征学习能力。该集成模型（称为EL-4）取得了0.8306的IoU值，优于U-Net的0.8065，并将Dice损失从FPN的0.1183降低至0.09019。本研究有望提升材料回收设施中废弃物分拣的效率，以最少的人工干预促进回收过程中原材料的更好获取，并提高整体处理能力。