Opto-Electronic Engineering, Volume. 52, Issue 3, 240275(2025)
Construction of convolutional neural network model for micro-scale bump on metal pipe fittings
Figures & Tables(17)
Fig. 1. Special metal pipe surface micro-scale bump detection system
Fig. 2. Comparison of original YOLOv9 and improved YOLOV9-MM network structure
Fig. 3. The downsampling operation. (a) Convolution downsampling; (b) Downsampling for maximum pooling; (c) Downsampling for average pooling
Fig. 8. Comparison of defect detection performance at different magnifications
Fig. 10. Thermal maps and visualization of the results of micro-scale bump detection on metal surfaces. (a) Original images; (b) The detection results of YOLOv9; (c) Thermal maps for YOLOv9 detection; (d) The detection results of YOLOv9-MM; (e) Thermal maps for YOLOv9-MM detection
Fig. 12. Thermal map and result visualization of PCB defect detection. (a) The test result of YOLOv9; (b) Thermal map for YOLOv9 detection; (c) Detection result of YOLOv9-MM; (d) Thermal map of YOLOv9-MM detection
Table 1. Models and specifications of components in the image acquisition system
View tableView in ArticleTable 1. Models and specifications of components in the image acquisition system
Instrument indicators and parameters An experimental instrument is needed to construct a micro-scale bump detection system for metal pipe fittings Industrial CCD camera Lens Ring light source Move the lever up and down 3D adjustment lever Fitting type Rotation table Instrument type MV-CS016-10UC WT-G0745 PRC-200360X-B1 LZ-500 HTZ120 KCM82-4NC 16 CM/5-100S/15 KG Parameter name Resolution/pixel Magnification/X Luminous power/W Range of movement/mm Stretch range/mm Width× Height/mm Rotational speed/rpm Parameter value 1440×1080 [0.7,4] [2.8,12.1] [0,461] [−10,100] 8.8 24 [0,1.54]
Table 2. Multi-magnification experimental parameters
View tableView in ArticleTable 2. Multi-magnification experimental parameters
Magnification 0.7× 1.0× 2.0× 3.0× 4.0× Exposure time/ms 10 15 20 25 30 Depth of field/mm 1.40 1.30 0.96 0.62 0.28 Field of view/mm (1/3CCD diagonal) 8.6 8.0 6.1 4.2 2.3
Table 3. Removal of auxiliary reversible branch experimental results
View tableView in ArticleTable 3. Removal of auxiliary reversible branch experimental results
Network structure mAP@0.5/% Param/M FLOPs/G YOLOv9s 65.2 48.6 238.9 - Auxiliary invertible branch 66.9 30.0 118.5
Table 4. Ablation experimental results
View tableView in ArticleTable 4. Ablation experimental results
Number MCDown Dynamic integration of deep features Inner_MDPIoU P/% R/% mAP@0.5/% FLOPs/G FPS/(f/s) Ⅰ 66.2 57.1 66.9 118.5 133 Ⅱ √ 70.1 61.7 68.7 101.8 108 Ⅲ √ 72.0 63.9 69.4 145.7 83 Ⅳ √ 74.3 57.7 69.1 116.8 138 Ⅴ √ √ 76.4 58.5 69.8 130.3 88 Ⅵ √ √ 69.9 57.9 69.3 144.6 92 Ⅶ √ √ 77.2 60.8 68.3 101.8 130 Ⅷ √ √ √ 71.1 61.5 70.2 129.3 90
Table 5. Comparative experimental results
View tableView in ArticleTable 5. Comparative experimental results
Method mAP@0.5/% FLOPs/G FPS/(f/s) Param/M Faster RCNN 56.9 36.4 18 24.1 YOLOv5s 62.1 15.9 131 13.7 YOLOv7 65.5 104.7 161 18.1 YOLOv8s 64.7 28.4 303 21.5 FCOS 59.7 33.5 25 53.0 YOLOv10s 65.8 24.8 45.9 8.2 YOLOv11s 65.3 21.3 53.2 9.4 YOLOv9s 65.2 238.9 87 48.6 YOLOv9-MM(ours) 70.2 129.3 90 22.7
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Zihao Liu, Guohao Tao, Feng Xue, Yebo Lu, Jun Yang. Construction of convolutional neural network model for micro-scale bump on metal pipe fittings[J]. Opto-Electronic Engineering, 2025, 52(3): 240275
Paper Information
Category: Article
Received: Nov. 26, 2024
Accepted: Feb. 17, 2025
Published Online: May. 22, 2025
The Author Email: Jun Yang (杨俊)
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