Application of Deep Learning Image Segmentation Algorithm in Product Defects
Keywords:
Deep learning, Image segmentation algorithm, Product defects, ApplicationAbstract
This paper investigates the application of deep learning image segmentation algorithms in product defect detection. Firstly, by analyzing the problems of traditional methods in product defect detection, the challenges faced by traditional methods are pointed out. Then, the advantages and applications of it in product defect detection were discussed in detail, providing reference for relevant personnel.
References
Wu, Z. (2024). Deep Learning with Improved Metaheuristic Optimization for Traffic Flow Prediction. Journal of Computer Science and Technology Studies, 6(4), 47-53.
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.
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.
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.
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.
Ren, Z. (2024). A Novel Topic Segmentation Approach for Enhanced Dialogue Summarization. World Journal of Innovation and Modern Technology, 7(4), 42-49.
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.
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.
An, F. P. , & Liu, J. E. . (2021). Medical image segmentation algorithm based on multilayer boundary perception-self attention deep learning model.
Feng, Z. , Min, Q. , & Hua, X. . (2024). Research on the application of deep learning in human spinal image segmentation. IOP Publishing Ltd.
Wu, Z. (2024). An Efficient Recommendation Model Based on Knowledge Graph Attention-Assisted Network (KGATAX).?arXiv preprint arXiv:2409.15315.
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.
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).
Wang, Z., Sun, W., Chu, Z. C., Zhang, Y., & Wu, Z. (2024). LLM for Differentiable Surface Sampling for Masked Modeling on Point Clouds.
He, C., Liu, M., Wang, Z., Chen, G., Zhang, Y., & Hsiang, S. M. (2022). Facilitating Smart Contract in Project Scheduling under Uncertainty—A Choquet Integral Approach. Construction Research Congress 2022, 930–939. https://doi.org/10.1061/9780784483961.097
He, C., Yu, B., Liu, M., Guo, L., Tian, L., & Huang, J. (2024). Utilizing Large Language Models to Illustrate Constraints for Construction Planning. Buildings, 14(8), 2511. https://doi.org/https://doi.org/10.3390/buildings14082511
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.
