Abstract
In printed circuit board (PCB) defect detection, there are some imbalances, which generally consists in feature level and sample level. The feature imbalance problem is caused by the semantic loss of features in the feature maps. The sample imbalance is caused by two aspects, defects of different scales and the incomplete utilization of negative sample information. These can affect the accuracy of defect detection. Resolving these imbalances is an important research direction to improve the accuracy of detection. To alleviate the impact of imbalance, a sample and feature equalization-based R-CNN detection algorithm (Feature and Simple Balance R-CNN, FaSB R-CNN) is proposed. It contains three innovative components: Feature Balance Processing (Feature Balance Algorithm and FPN), GA-RPN and IoU-Balanced Sampling. Using the above three components in the basic Faster R-CNN system, our method (FaSB R-CNN) has a large improvement in PCB defect detection. FaSB R-CNN achieves 8.18% higher precision on PCB Data set than the Faster R-CNN. Compared with other object detection algorithms, the FaSB R-CNN can also achieve relatively good results and has good practical engineering application value.
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References
Hui, S.Y.: Planar wireless charging technology for portable electronic products and Qi. Proc. IEEE 101(6), 1290–1301 (2013)
Li, J., Shrivastava, P., Zong, G., et al.: Printed circuit board recycling: a state-of-the-art survey. IEEE Trans. Electron. Packag. Manuf. 27(1), 33–42 (2004)
Onshaunjit, J., Srinonchat, J.: Algorithmic scheme for concurrent detection and classification of printed circuit board defects. Comput. Mater. Continua 71(1), 355–367 (2022)
Chang, P.C., Chen, L.Y., Fan, C.Y.: A case-based evolutionary model for defect classification of printed circuit board images. J. Intell. Manuf. 19(2), 203–214 (2008)
Wang, S.Y., Zhao, Y., Wen, L.: PCB welding spot detection with image processing method based on automatic threshold image segmentation algorithm and mathematical morphology. Circuit World 42(3), 97–103 (2016)
Kim, J., Seo, D., Moon, J.: Design and implementation of an HCPS-based PCB smart factory system for next-generation intelligent manufacturing. Appl. Sci. Basel 12(15), 7645 (2022)
Sezer, A., Altan, A.: Detection of solder paste defects with an optimization-based deep learning model using image processing techniques. Soldering Surf. Mount Technol. 33(5), 291–298 (2021)
Wang, J., Sun, K., Cheng, T., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349–3364 (2021)
Wu, Z., Pan, S., Chen, F., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2021)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: 29th Annual Conference on Neural Information Processing Systems, Montreal, Canada, pp. 770–778 (2016)
He, K., Gkioxari, G., Dollar, P., et al.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2020)
Oksuz, K., Cam, B., Kalkan, S., Akbas, E.: Imbalance problems in object detection: a review. IEEE Trans. Pattern Anal. Mach. Intel. 43(10), 3388–3415 (2020)
Li, Y.L., Wang, S.J., et al.: Feature representation for statistical-learning-based object detection: a review. Pattern Recogn. 48(11), 3542–3559 (2015)
Cha, Y.J., Choi, W., Oral, B.: Deep learning-based crack damage detection using convolutional neural networks. Comput. Aided Civil Infrastruct. Eng. 32(5), 361–378 (2017)
Cha, Y.J., Choi, W., Suh, G., et al.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput. Aided Civil Infrastruct. Eng. 33(9), 731–747 (2018)
Tao, X., Zhang, D., Wang, Z., Liu, X., et al.: Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans. Syst. Man Cybern. Syst. 50(4), 1486–1498 (2020)
Xue, Y., Li, Y.: A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects. Comput. Aided Civil Infrastruct. Eng. 33(8), 638–654 (2018)
Rw, D., Lh, D., Gp, L., Hong, L.: TDD-Net: a tiny defect detection network for printed circuit boards. CAAI Trans. Intell. Technol. 4(2), 110–116 (2019)
Chen, J.W., Liu, Z.G., Wang, H.R., et al.: Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans. Instrum. Meas. 67(2), 257–269 (2018)
Li, Y.T., Huang, H.S., Xie, Q.S., et al.: Research on a surface defect detection algorithm based on MobileNet-SSD. Appl. Sci. 8(9), 1678 (2018)
Zhang, C.B., Chang, C.C., Jamshidi, M.: Concrete bridge surface damage detection using a single-stage detector. Comput. Aided Civil Infrastruct. Eng. 35(4), 389–409 (2020)
Dai, J.F., Li, Y., He, K.M., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: 30th Conference on Neural Information Processing Systems, Barcelona, Spain (2013)
Wang, J., Chen, K., Yang, S., et al.: Region proposal by guided anchoring. In: 32nd IEEE Conference on Computer Vision and Pattern Recognition, pp. 2960–2969. IEEE Computer SOC, Long Beach, CA (2019)
Mao, Q.C., Sun, H.M., Liu, Y.B., et al.: Mini-YOLOv3: real-time object detector for embedded applications. IEEE Access 7, 133529–133538 (2019)
Wei, L., Dragomir, A., Dumitru, E., et al.: SSD: single shot MultiBox detector. In: 14th European Conference on Computer Vision, pp. 21–37. Springer-verlag Berlin, Amsterdam, Netherlands (2016)
Lin, T.Y., Dollar, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: 30th IEEE Conference on Computer Vision and Pattern Recognition, pp. 936–944, IEEE, Honolulu, HI (2017)
Acknowledgement
This work is partially supported by the Research Project Supported by Shanxi Scholarship Council of China (No. 2021-046), Natural Science Foundation of Shanxi Province (No. 202103021224056) and Shanxi Science and Technology Cooperation and Exchange Project (No. 202104041101030).
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Zheng, Z., Zhao, W., Wang, H., Xu, X. (2023). Surface Defect Detection of Electronic Components Based on FaSB R-CNN. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_40
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