A Lightweight Waste Detection Framework with Multi-Scale Feature Fusion and Dynamic Task Alignment for Real-Time Edge Deployment

Rapid urbanization and continuous population growth have made waste management a critical global challenge. In this study, we construct the Waste4 dataset and propose a lightweight waste detection framework, FLEX-YOLO, to improve detection accuracy and computational efficiency in complex environments. Specifically, an Efficient Multi-Scale Feature Fusion Network (EMBSFPN) is developed to enhance multi-scale feature representation for waste objects with varying scales. The backbone is further enhanced by incorporating MogaBlock into the C3k2 component to improve local–global feature modeling. Moreover, a Dynamic Task-Aligned Detection Head (DTADH) is introduced to strengthen the interaction between classification and localization through shared feature learning and dynamic alignment strategies. An Efficient Multi-Scale Attention (EMA) mechanism is incorporated before the detection head to suppress background interference and improve target feature discrimination. Experimental results on the Waste4 dataset demonstrate that FLEX-YOLO achieves 97.0% mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.50 (mAP@50) and 74.0% mAP@50–95. Compared with YOLOv11 and the Real-Time Detection Transformer (RT-DETR), FLEX-YOLO improves mAP@50 by 3.5 and 4.0 percentage points, respectively, and mAP@50–95 by 3.8 and 3.0 percentage points, respectively. Additional experiments on the Trash Detection Dataset (TDD) show that FLEX-YOLO improves mAP@50 and mAP@50–95 by 2.2 and 1.9 percentage points, respectively, compared with YOLOv11. Furthermore, deployment on Jetson Orin Nano and Android platforms demonstrates its real-time inference capability and practical applicability. The proposed framework provides an efficient approach for real-world waste detection under resource-constrained edge deployment scenarios.