Road Target Detection Algorithm Based on Improved YOLOv5

Aiming at the problem that the existing road target detection algorithm is difficult to deploy lightweight., this paper proposes an improved YOLOv5 algorithm for road target detection based on the YOLOv5 network framework. By reconstructing the network structure of the Backbone and Neck parts, the number of model parameters and calculations are reduced to make the model lightweight, by introducing Stem Block and CBAM, the model structure is optimized to improve the model detection accuracy, re-clustering Anchors to improve the target positioning ability, The comparison and ablation experiments were carried out using the parameter amount, calculation amount, AP, mAP, and FPS as evaluation indicators. While meeting the requirements of real-time detection, the algorithm in this paper has good target detection accuracy, and the parameter amount and calculation amount are reduced by 29.0% and 36.2% respectively compared with the original model, which is suitable for road target detection tasks.