Open AccessDetection of Metal Surface Defects Based on YOLOv4 Algorithm
Author(s) -
Haili Zhao,
Yang Ze-feng,
Jia Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1907/1/012043
Subject(s) - pyramid (geometry) , computer science , feature (linguistics) , position (finance) , pattern recognition (psychology) , artificial intelligence , feature extraction , object detection , algorithm , data mining , mathematics , philosophy , linguistics , geometry , finance , economics
To solve the problem of low recognition accuracy and low defect location accuracy in traditional detection of surface defects of metal materials, this paper innovates on the basis of YOLOv4 architecture, and studies the influence of adding feature pyramid network module to different position of model neck on detection algorithm. Experiments have shown that adding the feature pyramid network (FPN) module after sampling on the neck network can enhance the feature information expression ability of the feature map originally input to the detection head in the size of 80×80 and 40×40, and achieve better detection results, and achieve better detection results. The experimental results show that adding feature pyramid network module to the neck can effectively improve the detection accuracy of the algorithm. Finally, compared with the traditional YOLOv4 network, the average recognition accuracy of this model can reach 92.5% and the recognition accuracy is improved.