Optimization of vehicle detection based on video images
With the development of intelligent transportation systems and autonomous vehicles,the importance of vehicle detection technology has become increasingly prominent.Traditional object detection algorithms perform poorly on high-resolution,multi-scale,and complex background images captured by UAVs.To improve the detection accuracy of electric bicycles in UAV aerial images using the Yolov5 algorithm,enhancements in data acquisition and processing,model adjustment,and optimization are necessary.Firstly,diverse high-quality images need to be obtained,and the feature recognition capability of electric bicycles should be enhanced through improved annotation techniques and image augmentation methods.Secondly,the size and ratio of Anchor Boxes should be adjusted,and the K-means clustering algorithm should be introduced to calculate the optimal Anchors,thereby optimizing the model training process.By incorporating weighted loss functions and focal loss functions,the detection accuracy of hard-to-classify electric bicycles is improved.Experiments show that the optimized Yolov5 model outperforms the original model in detecting electric bicycles in the same environment,with accuracy improved from 72％to 83％and recognition efficiency increased by 23％.The optimized Yolov5 model provides a more accurate and efficient solution for electric bicycle detection in intelligent transportation systems.
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