Vehicle Target Detection Based on Improved YOLOv5 Algorithm
In recent years,vehicle detection has emerged as a pivotal area of investigation within intelligent transporta-tion systems.The complexities of real-world road conditions often pose considerable challenges to target detection ef-forts,particularly concerning the phenomena of target occlusion,overlap,and small targets.This paper introduces an enhanced vehicle detection model based on the YOLOv5 algorithm.Initially,the integration of the coordinate attention(CA)mechanism fortifies the detection network's capacity to accurately discern vehicle targets amidst densely popu-lated scenes.Subsequently,the implementation of Focal-EIOU Loss as an alternative loss function is delineated,con-tributing to refined bounding box localization and expedited loss function convergence.The culmination of this series of enhancements is the incorporation of omni-dimensional dynamic convolution(ODConv),an innovative approach that significantly refines feature extraction by harnessing the synergy of multiple convolutional kernels and multi-dimen-sional attention to vehicle characteristics.Experimentation on an self-made vehicle dataset elucidates that the pro-posed algorithmic enhancements result in a mean average precision(mAP)increment of 1.5%over the original algo-rithm,thereby proving its efficacy and superior performance in the domain of vehicle target detection.
deep learningYOLOv5attention mechanismloss functionomni-dimensional dynamic convolution(ODConv)
NETL
NSTL
万方数据
维普
