Traffic sign detection is a crucial task for autonomous driving perception systems, as it directly impacts vehicle path planning and safety decisions. Existing algorithms face challenges such as feature information attenuation and model lightweighting requirements in the detection of small traffic signs at long distances. To address these issues, this paper proposes a dual-pooling dynamic grouping (DPDG) module. This module dynamically adjusts the number of groups to adapt to different input features, combines global average pooling and max pooling to enhance channel attention representation, and uses a lightweight 3 × 3 convolution-based spatial branch to generate spatial weights. Based on a hierarchical optimization strategy, the DPDG module is integrated into the YOLOv10n network. Experimental results on the traffic sign dataset demonstrate a significant improvement in the performance of the YOLO-DPDG network: Compared to the baseline YOLOv10n model, mAP@0.5 and mAP@0.5:0.95 improved by 8.77% and 10.56%, respectively, while precision and recall were enhanced by 6.16% and 6.62%, respectively. Additionally, inference speed (FPS) increased by 11.1%, with only a 4.89% increase in model parameters. Compared to the YOLOv10-Small model, this method achieves a similar detection accuracy while reducing the number of model parameters by 64.83%. This study provides a more efficient and lightweight solution for edge-based traffic sign detection.

We propose AOW-YOLO, a novel model designed to address the challenge of detecting smoking behavior in complex construction site environments, which is particularly difficult due to the typically small size of objects and cluttered backgrounds. AOW-YOLO is built upon YOLO11n through the following steps. First, we develop a novel loss function, Adaptive Occlusion and Weighting IoU (AOWIoU), which dynamically optimizes sample quality gradient allocation using multiple mechanisms, thereby overcoming the reduced sensitivity of CIoU to spatial scale errors. Second, we introduce the Spatial Grouped-Pointwise (SGP) convolution module. By integrating a channel-adaptive grouping mechanism, it minimizes information loss during downsampling, alleviating issues related to low resolution and feature degradation. Finally, our SGP module replaces the original backbone in LCNet. Through pointwise convolution, this method enables cross-group information fusion and expands channel dimensions, effectively solving feature integration problems caused by excessive channel segmentation. Experimental results show that on the smoking detection dataset, the AOW-YOLO model surpasses existing lightweight models (e.g., PP-LCNet) in both mAP50 and mAP50:95 metrics, while achieving roughly 31.6% faster inference speed than YOLO11n. This approach provides new insights for designing lightweight detection models and has broad potential applications in construction site safety management.