Research on a lightweight traffic sign detection algorithm based on GCL-YOLOv8

A lightweight traffic sign detection algorithm based on YOLOv8n improved GCL-YOLOv8 is proposed to address the issues of low accuracy and large parameter count in the process, resulting in long computation time and complexity. Firstly, a new module GRA is designed using GhostModule, RepConv, and ECA channel attention to replace the original C2f module, balancing inference efficiency and feature expression ability, reducing computational complexity while also considering accuracy; Secondly, the CARAFE lightweight upsampling module is used to replace ordinary upsampling, and the content aware recombination mechanism is applied to effectively amplify and preserve feature details; Then, by adding an LADH detection head, the parameters and computational complexity can be effectively reduced; Finally, Wise MPDIOU was used to replace the original CIoU loss function, simplifying the calculation process of the loss function and improving convergence speed and regression accuracy. Compared with the basic YOLOv8n algorithm, this algorithm improved accuracy P by 2.5%, mAP50 by 2.7%, parameter Params by 42.3%, and GFLOPs by 48.8% on the TT100K dataset. Proved the lightweight and accuracy of the improved algorithm.