Lightweight Vehicle Detection System Based on the Improved YOLOv5s
Vehicle detection based on deep learning plays an important role in smart transportation.The existing model structure which is complex and computationally intensive makes it difficult to deploy on edge devices of embedded systems.This paper proposed a lightweight vehicle detection algorithm based on YOLOv5s.YOLOv5s was improved through GhostNet and pruning optimization strategies to achieve lightweight system and real-time detection;and the introduction of the loss function Focal-EIoU Loss solved the problem of sample imbalance and aspect ratio blur define the problem,thereby improving the performance of object detec-tion.Experimental results on the UA-DETRAC data set are as follows.Compared with the original YOLOv5s al-gorithm,the proposed algorithm reduces the number of parameters,model volume and FLOPs by 63%,50.6%and 64.7%respectively,and the detection speed increases by 50%,while maintaining high precision and recall rate.This paper provides a real-time algorithm choice for edge detection equipment with limited space,energy,and resources.
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