Abstract
In order to improve detection accuracy while minimizing computational overhead, a modified algorithm is proposed based on the YOLO11n baseline. The innovation incorporates a lightweight ADown module into the P4 and P5 layers of the backbone network, strategically reducing computational complexity. Simultaneously, a multi-scale attention mechanism with parallel structure is integrated into the detection head to enhance feature representation, while a micro-detection head is appended to specifically improve the detection of tiny objects. Based on the classic metrics, including parameter count, mAP@50, mAP@50-95, recall, and FPS, the ablation experiments are performed to validate the improvement of the improved algorithm on the CCTSDB2021 dataset. Furthermore, comparative experiments against traditional YOLO variants are conducted on both CCTSDB2021 and TT100K-2021 datasets. Experimental results demonstrate significant improvements across all evaluated metrics for the improved algorithm, highlighting its exceptional capability to balance high accuracy with minimal computational complexity.