Tehnički vjesnik, Vol. 29 No. 5, 2022.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20211219032823
Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals
Bo Lv
; Guangdong provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
Xiangdong Gao
; Guangdong provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
Sang Feng
; Guangdong provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
Jinhao Yuan
; Guangdong provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
Sažetak
The surface defects of automobile door seals are mainly detected manually at present, which is costly and has low efficiency. Therefore, a deep learning automatic detection algorithm of automobile door seals is studied in this paper. At first, the defects are classified and the data set is made according to the geometric characteristics of the defects. While enhancing the data set, the K-means clustering algorithm is used to cluster the target annotation frame in the data set, and the anchor that matches with the surface defect size of the seal is obtained. Finally, in view of the characteristics of large variation range of defect size, the target detection algorithm YOLOV3 is selected as the basic framework. Meanwhile, considering the high proportion of small and medium-sized targets in defects, the output scale is introduced at 4 times of the sampling position of YOLOV3 backbone network. In order to further enhance the correlation between the extracted features and the channel, spatial position and coordinate position, the feature fusion and attention mechanism module is constructed. The test results show that the mean of the average precision is improved by 4.81% compared with YOLOV3.
Ključne riječi
attention mechanism; convolutional neural network; door seal; feature fusion; surface defect
Hrčak ID:
281661
URI
Datum izdavanja:
15.10.2022.
Posjeta: 783 *