Lightweight Algorithm for Part Recognition Based on YOLOv5
In order to solve the problems of excessive parameter amount,slow detection speed and low de-tection accuracy of existing deep learning-based part recognition models,this paper proposes a part recogni-tion algorithm combining a lightweight network and Transformer based on the YOLOv5 model.Firstly,a lightweight trunk feature extraction network is designed to reduce the number of parameters and computa-tion of the network and to improve the inference speed;secondly,the Transformer module is fused with the C3 module to form the C3TR module to enhance the detection of small targets;finally,the noise purifica-tion module is introduced to improve the accuracy of the part recognition model by filtering the noise.The average detection accuracy and average recall of the model reach 86.7%and 85.5%,respectively,which are improved and 24.2%and 17.4%,respectively,compared with the original model.The experimental re-sults show that the improved model has faster detection speed and higher recognition accuracy while achie-ving model lightweighting.
part identificationmodel light-weightingYOLOv5Transformer modulenoise cleaning module
NETL
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