With the increase in the number of automobiles,road condition analysis technology in intelligent transportation has become increasingly important.It is widely used in the fields of Object Detection,autonomous driving and automobile collision hazard early warning,and has significant research significance.Based on the Python language,this paper proposes an improved model combining YOLOv5s and Attention Mechanism,which realizes road condition detection and traffic flow analysis and can be widely used in the scene of automobile collision hazard level early warning.It can provide more appropriate protection for passengers in combination with the intelligent recognition of passengers'sitting position.The system supports users to upload images,and uses the improved model to identify automobiles and count traffic flow to realize the analysis of road condition.The experiment uses a dataset of nearly 80 000 images,of which 80%is used for training and 20%for testing.The results show that the improved model has a significant improvement in Object Detection performance,and its accuracy and efficiency are better than those of the basic model,which has a certain practical significance in the automobile collision early warning and other fields.
Attention Mechanismroad condition analysisObject Detectioncollision early warning
维普
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