MLGA-CDRF-YOLO: A Lightweight Target Detection Method of Multi-Scale Group Attention and Channel Dynamic Residual...

A lightweight multi-scale group attention and channel dynamic residual fusion method is proposed to address the challenge of detecting extremely small encoded targets in particle accelerator multipole magnet collimation. In this scenario, the encoded targets occupy merely 0.003% to 0.009% of the total image pixels, rendering conventional visual techniques inadequate for capturing such fine-grained features. The paper proposes MLGA-CDRF-YOLO, an improved detection framework based on YOLOv11s that integrates a lightweight multi-scale group attention mechanism with channel-wise dynamic residual fusion. Specifically, the C2f-CDRF module combines lightweight channel attention with a dynamic residual structure, enhancing feature representation while reducing parameter count and computational complexity. The Multi-scale Lightweight Group Attention (MLGA) module splits input features into groups processed in parallel through a channel attention branch and a multi-scale spatial attention branch, with ChannelShuffle enabling cross-group information exchange, to improve the model's feature representation capability and generalization performance for complex scenes. Experimental results on the encoded target dataset demonstrate that MLGA-CDRF-YOLO achieves an mAP@0.5 of 95.5%, a precision of 95.8%, and a recall of 88.5%, with only 3.41 M parameters and 14.8 GFLOPs, achieving a competitive and comprehensive balance between accuracy and computational cost. Furthermore, evaluation on the NEU-DET dataset confirms the model's stable generalization performance across diverse detection tasks.