Object-to-Pixel Visual Understanding for Automated Packaging Waste Sorting in Sustainable Educational Recycling Ecosystems

Automated packaging waste sorting requires rapid object localization and accurate boundary delineation under irregular shapes, deformation, reflection, dense stacking, and partial occlusion. This study presents an edge AI-enabled object-to-pixel framework in which PSSM-YOLO performs object detection and CAA-DeepLabv3+ independently performs semantic segmentation on complete RGB images. EPWaste contains 4528 RGB images and 11,384 annotated object instances from ten representative categories, with bounding-box and pixel-level annotations for detection and segmentation. PSSM-YOLO combines multi-head self-attention, an SRG multi-scale feature fusion module, Shuffle Attention, and a progressively refined detection head. CAA-DeepLabv3+ combines channel attention aggregation with CARAFE content-aware upsampling to reconstruct weak and irregular boundaries. On EPWaste, PSSM-YOLO achieved 87.2% mAP50, 74.1% mAP50:95, 93.6% precision, and 85.8% recall. CAA-DeepLabv3+ achieved 82.4% mIoU, 83.6% mPA, 83.3% mPrecision, and 83.6% mRecall on complete images. For 640 x 640 input, PSSM-YOLO and CAA-DeepLabv3+ required 12.8 M and 6.7 M parameters and 32.6 and 19.8 GFLOPs, respectively. On an NVIDIA Jetson Orin Nano, INT8 quantization with structured pruning reduced PSSM-YOLO latency to 11.1 ms (90.1 FPS) and power to 5.1 W. In a laboratory-scale conveyor test, the prototype correctly sorted 938 of 974 objects (96.30%), with a throughput of 58.3 objects per minute and an end-to-end latency of 312 ms. These controlled results support the feasibility of the framework while motivating broader cross-environment validation.