An Optimized Leakage-Aware YOLO-Based Deep Learning Framework for Instance Segmentation and Environmental Impact Assessment of Mixed Metal and Glass Waste

Accurate instance segmentation of recyclable waste is important for automated sorting and circular economy applications. Mixed metal and glass municipal waste is challenging because metal surfaces are often reflective, while glass objects have transparent boundaries and high visual variability. This paper presents a controlled computer vision framework that uses instance segmentation as an object-level perception layer for mixed metal and glass waste and links eligible detections to screening-level WARM-based environmental interpretation. A seven-class annotated dataset of 1667 images was evaluated using a fixed train/validation/test split. Model development used the validation split for architecture screening, optimization tuning, transfer learning analysis, seed selection, and NMS selection, and the held-out test set was used only for final evaluation. Using mask mAP@50:95 as the primary segmentation metric, the selected YOLOv8m-seg model achieved 0.9435 on the locked test set and obtained a higher mask mAP@50:95 than Mask R-CNN and RF-DETR-Seg under the same locked test protocol. Image-level bootstrap 95% confidence intervals were also used to characterize uncertainty around the locked test segmentation comparison. For environmental reporting, mass-bearing detections were mapped to class-specific mass priors and EPA WARM v16 factors rather than direct mask area-to-mass conversion; the masks were used for instance-level separation, localization, and visual verification, not as direct physical mass measurements. Under the stated assumptions, the framework produced screening-level environmental estimates of 1113.57 MJ of energy savings and 70.20 kg CO2e of avoided emissions, decreasing to 445.67 MJ and 28.62 kg CO2e after applying recycling rate priors. Overall, the framework provides a leakage-aware workflow linking instance segmentation to material-specific WARM-based environmental screening.