Application of Artificial Intelligence Technology in Industrial Defect Detection
Keywords:
Artificial intelligence, Testing technology, Industrial production, Defect detectionAbstract
Defects are inevitable in the production process of products, and these defects can have an impact on the appearance and even functionality of the product. Defect detection is of great significance in improving product quantity, ensuring industrial safety, and environmental protection. This article analyzes from the perspective of commonly used defect point detection methods and introduces the problems and solutions currently encountered in industrial defect point detection. With the continuous development of new detection technologies and artificial intelligence, industrial defect point detection will move towards higher accuracy and efficiency, creating more value for enterprises. At the same time, the development of detection technology will promote the intelligent process of industrial production.
References
Zheng, H., Wang, B., Xiao, M., Qin, H., Wu, Z., & Tan, L. (2024). Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function. arXiv preprint arXiv:2408.11839.
Wang, Z., Chu, Z. C., Chen, M., Zhang, Y., & Yang, R. (2024). An Asynchronous LLM Architecture for Event Stream Analysis with Cameras. Social Science Journal for Advanced Research, 4(5), 10-17.
Xiong, Y. , Liang, L. , Wang, L. , She, J. , & Wu, M. . (2020). Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Computers and Electronics in Agriculture, 177(4), 105712.
Wang, Z., Sun, W., Chu, Z. C., Zhang, Y., & Wu, Z. (2024). LLM for Differentiable Surface Sampling for Masked Modeling on Point Clouds.
An, F. P. , & Liu, J. E. . (2021). Medical image segmentation algorithm based on multilayer boundary perception-self attention deep learning model.
Lim, S. , Kim, Y. J. , & Kim, K. . (2020). Three-dimensional visualization of medical image using image segmentation algorithm based on deep learning. Journal of Korea Multimedia Society, 23, 468-475.
Xiong, J. W. M. . (2020). Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Computers and Electronics in Agriculture, 177(1).
Xu, Y., Shan, X., Guo, M., Gao, W., & Lin, Y. S. (2024). Design and Application of Experience Management Tools from the Perspective of Customer Perceived Value: A Study on the Electric Vehicle Market. World Electric Vehicle Journal, 15(8), 378.
Zhu, Z., Wang, Z., Wu, Z., Zhang, Y., & Bo, S. (2024). Adversarial for Sequential Recommendation Walking in the Multi-Latent Space.?Applied Science and Biotechnology Journal for Advanced Research,?3(4), 1-9.
Feng, Z. , Min, Q. , & Hua, X. . (2024). Research on the application of deep learning in human spinal image segmentation. IOP Publishing Ltd.
Shen, Z. (2023). Algorithm Optimization and Performance Improvement of Data Visualization Analysis Platform based on Artificial Intelligence. Frontiers in Computing and Intelligent Systems, 5(3), 14-17.
Wang, Z., Zhu, Y., Chen, M., Liu, M., & Qin, W. (2024). Llm connection graphs for global feature extraction in point cloud analysis. Applied Science and Biotechnology Journal for Advanced Research, 3(4), 10-16.
Ren, Z. (2024). Enhanced YOLOv8 Infrared Image Object Detection Method with SPD Module. Journal of Theory and Practice in Engineering and Technology, 1(2), 1–7. Retrieved from https://woodyinternational.com/index.php/jtpet/article/view/42
Wang, Z., Yan, H., Wang, Y., Xu, Z., Wang, Z., & Wu, Z. (2024). Research on autonomous robots navigation based on reinforcement learning.?arXiv preprint arXiv:2407.02539.
Ren, Z. (2024). A Novel Topic Segmentation Approach for Enhanced Dialogue Summarization. World Journal of Innovation and Modern Technology, 7(4), 42-49.
Yang, H., Zi, Y., Qin, H., Zheng, H., & Hu, Y. (2024). Advancing Emotional Analysis with Large Language Models. Journal of Computer Science and Software Applications, 4(3), 8-15.
Ji, H., Xu, X., Su, G., Wang, J., & Wang, Y. (2024). Utilizing Machine Learning for Precise Audience Targeting in Data Science and Targeted Advertising. Academic Journal of Science and Technology, 9(2), 215-220.
Chen, G., He, C., Hsiang, S., Liu, M., & Li, H. (2023). A mechanism for smart contracts to mediate production bottlenecks under constraints. 31st Annual Conference of the International Group for Lean Construction (IGLC), 1232–1244. https://doi.org/10.24928/2023/0176
Wu, Z. (2024). An Efficient Recommendation Model Based on Knowledge Graph Attention-Assisted Network (KGATAX).?arXiv preprint arXiv:2409.15315.""
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification. Journal of Theory and Practice of Engineering Science, 4(02), 15-22. https://doi.org/10.53469/jtpes.2024.04(02).03
Tian, Q., Wang, Z., Cui, X. Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv preprint arXiv:2409.13626.