Recognition of mechanical parts based on improved YOLOv4-Tiny algorithm
In this article,in order to ensure accurate and rapid recognition of mechanical parts,a method based on the YOLOv4-Tiny algorithm is proposed.It combines the attention mechanism and the K-means++clustering algorithm,with the CSPDarknet53-Tiny network as the mainstream;Convolution Block Attention Module(CBAM)and Global Attention Mechanism(GAM)are added to the connection between the CSPDarknet53-Tiny main network and the feature pyramid as well as the upper sampling point.When the main network is not affected,the feature information of each channel is compressed and extracted once again.As a result,the redundant feature information is filtered out,the key feature information is retained,and the weight is real-located.Then,a set of prior frame parameters matching the data set of mechanical parts is obtained with the help of the K-means++clustering algorithm.The experimental results show that the compared with the traditional YOLOv4-Tiny algorithm,the im-proved YOLOv4-Tiny algorithm has desirable real-time performance,with the average recall rate and the average precision rate of 99.43％and 99.41％respectively.This algorithm is helpful to detect and reeognize the mechanical parts in an accurate manner.
YOLOv4-Tiny algorithmrecognition of mechanical partCBAMGAMK-means++clustering algorithm
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NSTL
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