Research on Improved YOLOv8 Detection Algorithm for Traffic Scenarios
In recent years,with the increasing popularity of new energy vehicles,the require-ments for technologies such as assisted driving and autonomous driving have become more strin-gent.However,in real driving scenarios,the complexity of road environments is high,especially in the case of occlusion.The detection accuracy of existing algorithms is not high,and the com-puting power of on-board computers is limited.In response to these issues,this paper proposes three improvement schemes.The improved model has a computational cost of only 6.1 GFLOPs under the BDD100k dataset,which is 75％of the original model,while Map50 has increased by 8.4％.The research results provide reference and basis for subsequent target recognition of as-sisted driving and deployment on mobile devices.
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