Research on Highway Pavement Information Recognition Algorithm Based on Improved YOLOv4
The acquisition and recognition of highway pavement information is one of the key technologies for con-structing intelligent highway systems.As for problems of time-consuming efforts,poor recognition accuracy,and difficulty in end-to-end application in existing highway pavement information detection algorithms,an improved YOLOv4 algorithm has been proposed.In order to enhance the model's generalization capability,the distance be-tween bounding boxes and prior boxes is measured by IoU value to obtain an improved k-means clustering algorithm forms,which was applied to anchor box clustering of the road surface scatter and disease data;In order to improve the network feature description capability,lightweight CBAM modules with combination of channel and spatial at-tention mechanisms are added to the last three branches of the feature enhancement network of PANNet,ensuring plug-and-play of the modules in the existing network architecture;In order to save parameters and computational power,channel pruning is performed on the model after sparse training.In order to meet the high recognition accu-racy requirements for small objects in road detection tasks,the iterative optimization of the model pruning rate is carried out to further realize end-to-end application of the highway pavement information recognition algorithm.Ex-perimental results show that the mAP@0.5 and mAP@0.75 of the improved YOLOv4 network model increased by 0.78％and 1.06％respectively compared with the original model.Frames per second(FPS)reaches 34.85.The per-formance requirements of automatic recognition is satisfied;With a 0.4 pruning rate,the pruned model showed good overall performance.When the mAP@0.5 of the model is 98.3％,compared with the original model,storage space,GFLOPs and the total number of parameters was reduced by 47.6％,34.0％and 51.4％respectively,and FPS was increased by 6.3％,which significantly reduce computational complexity and memory usage.The research results can be applied to the construction of the road network perception capability system of intelligent highways to achieve efficient and accurate collection of highway pavement information.
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