Improved YOLO11n for traffic light small object detection

Abstract

To address the challenges of high detection difficulty caused by small target size and complex background environments of traffic lights in urban road scenarios, this paper proposes a YOLO11n-based traffic light detection algorithm, named YOLO11n-PRE. Based on the baseline YOLO11n, the proposed algorithm first replaces the original C3k2 module in the backbone network with the C3k2-RCB module, which enhances deep feature extraction capability while maintaining lightweight via efficient residual connection and feature recalibration mechanism. Secondly, a P2 small object detection layer is introduced to fuse shallow high-resolution feature information, expanding the original 3-scale detection architecture to 4-scale detection, which significantly strengthens the network’s perception and positioning ability for micro-scale traffic lights. Finally, an ECA efficient channel attention module is embedded into the PANet feature fusion path, which enhances key feature response and suppresses redundant information via adaptive weight calibration in the channel dimension. Experimental results show that compared with the baseline YOLOv11n, YOLO11n-PRE achieves a 1.9 percentage point improvement in mAP@0.5, with precision and recall increased by 1.8 and 2.3 percentage points respectively, and the parameter count is only 2.8M, which fully meets the requirements of real-time and efficient detection.