International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 16, No. 4, 8 Aug. 2024
Cover page and Table of Contents: PDF (size: 1509KB)
Author(s)
Venkatesh Khemlapure
1
Ashwini Patil
1,*
Nikita Chavan
1
Nisha Mali
1
Index Terms
Defect Detection, CNN, YOLO, Alexnet, Mobile Application, Binary, Multiclass, Classification
Abstract
To maximize production efficiency, product quality control is paying more attention to the quick and reliable automated quality visual inspection. Product defect detection is a critical part of the inspection process. Manual defect detection has a lot of flaws that can be overcome using a deep learning approach. In this paper we have proposed and implemented the deep learning models to detect defects in the manufactured product. Two types of classification, i.e., binary and multiclass classification, is done using CNN, AlexNet, and YOLO algorithms. For the binary classification which is just used to check whether there is a defect in the product, we have proposed three different architectures of CNN, out of which the third CNN model gave 99.44% and 97.49% for training and testing, respectively. We also tested the AlexNet model and got accuracy of 97.6%. And for the multiclass classification that is used for identification of type(s) of defects, the YOLOv8 model is proposed and implemented, which gives better results by attaining a remarkable accuracy of 98.7% for multiclass classification. We also designed and developed the Android Application, which is used on the field for defect detection in the manufacturing industry.
Cite This Paper
Venkatesh Khemlapure, Ashwini Patil, Nikita Chavan, Nisha Mali, "Product Defect Detection Using Deep Learning", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.4, pp.39-54, 2024. DOI:10.5815/ijisa.2024.04.03
Reference
[1]Senthikumar, M., V. Palanisamy, and J. Jaya. "Metal surface defect detection using iterative thresholding technique." In Second International Conference on Current Trends In Engineering and Technology-ICCTET 2014, pp. 561-564. IEEE, 2014.
[2]Liu, Jing, Guocheng Xu, Lei Ren, Zhihui Qian, and Luquan Ren. "Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network." The International Journal of Advanced Manufacturing Technology 90 (2017): 2581-2588.
[3]Wang, Tian, Yang Chen, Meina Qiao, and Hichem Snoussi. "A fast and robust convolutional neural network-based defect detection model in product quality control." The International Journal of Advanced Manufacturing Technology 94 (2018): 3465-3471.
[4]Lin, Zhongkang, Zhiqiang Guo, and Jie Yang. "Research on texture defect detection based on faster-RCNN and feature fusion." In Proceedings of the 2019 11th International Conference on Machine Learning and Computing, pp. 429-433. 2019.
[5]Yang, Jing, Shaobo Li, Zheng Wang, Hao Dong, Jun Wang, and Shihao Tang. "Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges." Materials 13, no. 24 (2020): 5755.
[6]Dong, Xinghui, Christopher J. Taylor, and Tim F. Cootes. "Defect detection and classification by training a generic convolutional neural network encoder." IEEE Transactions on Signal Processing 68 (2020): 6055-6069.
[7]Boikov, Aleksei, Vladimir Payor, Roman Savelev, and Alexandr Kolesnikov. "Synthetic data generation for steel defect detection and classification using deep learning." Symmetry 13, no. 7 (2021): 1176.
[8]Wen, Qiaodi, Ziqi Luo, Ruitao Chen, Yifan Yang, and Guofa Li. "Deep learning approaches on defect detection in high resolution aerial images of insulators." Sensors 21, no. 4 (2021): 1033.
[9]Zhao, Weidong, Feng Chen, Hancheng Huang, Dan Li, and Wei Cheng. "A new steel defect detection algorithm based on deep learning." Computational Intelligence and Neuroscience 2021 (2021): 1-13.
[10]Bhatt, Prahar M., Rishi K. Malhan, Pradeep Rajendran, Brual C. Shah, Shantanu Thakar, Yeo Jung Yoon, and Satyandra K. Gupta. "Image-based surface defect detection using deep learning: A review." Journal of Computing and Information Science in Engineering 21, no. 4 (2021): 040801.
[11]Schmedemann, Ole, Melvin Baaß, Daniel Schoepflin, and Thorsten Schüppstuhl. "Procedural synthetic training data generation for AI-based defect detection in industrial surface inspection." Procedia CIRP 107 (2022): 1101-1106.
[12]Lim, JiaYou, JunYi Lim, Vishnu Monn Baskaran, and Xin Wang. "A deep context learning based PCB defect detection model with anomalous trend alarming system." Results in Engineering 17 (2023): 100968.
[13]Singh, Swarit Anand, and K. A. Desai. "Automated surface defect detection framework using machine vision and convolutional neural networks." Journal of Intelligent Manufacturing 34, no. 4 (2023): 1995-2011.
[14]Dwivedi, Divyanshi, K. Victor Sam Moses Babu, Pradeep Kumar Yemula, Pratyush Chakraborty, and Mayukha Pal. "Identification of surface defects on solar pv panels and wind turbine blades using attention based deep learning model." Engineering Applications of Artificial Intelligence 131 (2024): 107836.