DRSE‐YOLO: Efficient and Lightweight Architecture for Accurate Waste Detection
Guangling Sun, Fenqi Zhang · IET Image Processing · 2025
ABSTRACT This paper introduces DRSE‐YOLO, an efficient waste detection model designed to address detection accuracy and lightweight design challenges. The RCCA module in the model's neck enhances multi‐scale feature representation, thereby improving detection performance. The DySample module optimizes upsampling through adaptive point‐sampling, reducing computational demands and improving resource efficiency. The Slim‐Neck module is applied to select convolutional layers and C2f modules to streamline the model and enhance computational efficiency. The ECC‐Head integrates asymmetric depth convolution, point convolution, and an attention mechanism, balancing accuracy with reduced parameters and computational load. Evaluated on a custom dataset comprising 46 waste classes and approximately 25,000 images, DRSE‐YOLO achieves significant improvements over YOLOv8n, including a higher [email protected] (+1.59%) and [email protected]:95 (+2.08%), alongside a reduced parameter count (2.43 M vs. 3.2 M) and GFLOPs (5.8 vs. 8.2, a 24.4% reduction). These results underscore DRSE‐YOLO's efficiency and accuracy.