Xiangyuan Zhu
ABSTRACT
As an essential component of modern machines, printed circuit board (PCB) is widely used in various electronic products. Its quality significantly affects the quality of products. However, the production process of PCB is often accompanied with defects. In this paper, a defect detection algorithm is proposed. Data augmentation such as flipping, shifting, brightness adjustment, rotation, and Guass noise are carried out to diversify the dataset. You only look once (YOLO) v5s is then introduced to detect the PCB defects. Through parameter tuning and optimization, a trained detection model is achieved. F1-score and mean average precision (mAP) are used to assess the performance of the model. The experiment results show that the mAP and F1-score are 99.3% and 99.0%, respectively. The model developed based on YOLO-v5s algorithm can achieve superior performance, which is competent to detect the defects of PCBs.
- Yoon, H., JoonJae, L.: Pcb component classification algorithm based on yolo network for pcb inspection. Journal of Korea Multimedia Society 24(8), 988–999 (2021).Google Scholar
- Adibhatla, V.A., Chih, H.C., Hsu, C.C., Cheng, J., Abbod, M.F., Shieh, J.S.: Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once. Mathematical Biosciences and Engineering (4), 4411–4428 (2021).Google ScholarCross Ref
- Lin, Y.L., Chiang, Y.M., Hsu, H.C.: Capacitor detection in pcb using yolo algorithm. In: 2018 International Conference on System Science and Engineering (ICSSE) (2018).Google ScholarCross Ref
- Liu, Z., Qu, B.: Machine vision based online detection of pcb defect. Microprocessors and Microsystems 82(9), 103807 (2021).Google ScholarDigital Library
- Li, J., Su, Z., Geng, J., Yin, Y.: Real-time detection of steel strip surface defects based on improved yolo detection network. IFAC-PapersOnLine 51(21), 76–81 (2018), https://www.sciencedirect.com/science/article/pii/S2405896318321001, 5th IFAC Workshop on Mining, Mineral and Metal Processing MMM 2018.Google ScholarCross Ref
- Zhang, F., Qiao, N., Li, J.: A pcb photoelectric image edge information detection method. Optik 144, 642–646 (2017), https://www.sciencedirect.com/science/article/pii/S0030402617308112.Google ScholarCross Ref
- Ding, R., Dai, L., Li, G., Liu, H.: Tdd-net: A tiny defect detection network for printed circuit boards. CAAI Transactions on Intelligence Technology 4(2), 110–116 (2019).Google ScholarCross Ref
- Faghih-Roohi, S., Siamak, H., Nez, A., Babuska, R., Schutter, B.D.: Deep convolutional neural networks for detection of rail surface defects. In: International Joint Conference on Neural Networks (IJCNN 2016) (2016).Google ScholarCross Ref
- Liu, Y., Liu, S., Xu, J., Kong, X., Xie, L., Chen, K., Liao, Y., Fan, B., Wang, K.: Forest pest identification based on a new dataset and convolutional neural network model with enhancement strategy. Computers and Electronics in Agriculture 192, 106625 (2022) Prokop, Emily. 2018. The Story Behind. Mango Publishing Group. Florida, USA.Google Scholar
- Soukup, D., Huber-Mrk, R.: Convolutional neural networks for steel surface defect detection from photometric stereo images. Springer, Cham (2014).Google Scholar
- Zhao, W., Lu, H., Wang, D.: Multisensor image fusion and enhancement in spectral total variation domain. IEEE Transactions on Multimedia 20(4), 866–879 (2018).Google ScholarDigital Library
- Zhu, L., Spachos, P.: Support vector machine and yolo for a mobile food grading system. Internet of Things 13, 100359 (2021).Google ScholarCross Ref
Index Terms
- An Intelligent Defect Detection Algorithm for PCB based on Deep Learning
Recommendations
Method of PCB defect detection with yolov5 algorithm by adding transformer module
CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software EngineeringFor the current problems of low detection accuracy and slow detection speed of PCB board defect detection, this paper proposes a method of PCB defect detection by YOLOv5 algorithm with Transformer module added. The algorithm is using Transformer encoder ...
Machine vision based online detection of PCB defect
AbstractThe traditional PCB defect on-line detection has the problems of long detection time and poor accuracy of detection results. Therefore, a key technology of PCB defect online detection based on machine vision is proposed. Firstly, image retrieval ...
PCB Defect Detection Method Based on Improved RetinaNet
Machine Learning for Cyber SecurityAbstractIn the electronic industry product quality control, PCB defect detection is a crucial part, which has the characteristics of small defect size and high similarity. The existing defect detection methods are still not good enough for detecting small ...
Comments