GCA-YOLO: An Edge-optimized Traffic Sign Detection Model

To address the challenges of small target features being less prominent, susceptibility to background interference, and sample imbalance in road traffic sign detection, which leads to insufficient model detection accuracy, as well as the high complexity of current object detection models that struggle to operate efficiently on resource-constrained edge devices, we propose a traffic sign detection model based on GCA-YOLO. By adding small target detection layers and removing large target detection layers, the model enhances its small target detection capabilities and reduces its parameter size. The introduction of the T-BiFPN (Tiny-BiFPN) structure improves multi-scale feature fusion, while the C2f-CP module increases computational efficiency on edge devices. The GCA (Global Coordinate Attention) mechanism enhances feature extraction, and the Focaler-CIoU loss function enables the model to focus more on difficult samples and accelerate the convergence of bounding boxes. Experimental results show the superiorities of the proposed GCA-YOLO that compared to YOLOv8n, GCA-YOLO improves precision, recall, mAP@50, and mAP@50:95 by 8.6%, 6.1%, 8.7%, and 6.2%, respectively, while reducing the model's parameter count and size by 38.57% and 33.21%, respectively.