An improved YOLOv8 detection algorithm for traffic signs

To address the current issues of slow detection speed and poor accuracy for small targets in traffic sign detection, an optimized model based on YOLOv8 is proposed. This model integrates a small target detection layer, merging deep and shallow semantics to reduce semantic loss due to scale inconsistency. Additionally, it employs the lightweight EfficientNetV2 as the backbone network to decrease model parameters and enhance performance. The Coordinate Attention Mechanism is also incorporated, improving feature localization. Testing on the TT100K dataset, this model surpasses YOLOv8, with an accuracy increase of 7.1%, mAP@0.5 increased by 5.8%, a weight file of 5.92MB, and a detection rate of 279FPS. The model shows improved accuracy and performance while maintaining a simpler structure.