Research on the application of deep learning-based machine vision in automated inspection

[1] Zhenning, Y., Han, L. J., & Sai, Z. (2023). Correction to: research on simulation of 3d human animation vision technology based on an enhanced machine learning algorithm. Neural computing & applications. Search in Google Scholar

[2] Ye, H. (2023). Intelligent image processing technology for badminton robot under machine vision of internet of things. International journal of humanoid robotics(6), 20. Search in Google Scholar

[3] Gao, Z., He, S., Shi, X., & Xu, J. (2024). A leakage-suppressed capacitive-feedback amplifier scheme for event-based vision sensors in scaled-down technology. Microelectronics Journal, 147. Search in Google Scholar

[4] Koskinopoulou, M., Raptopoulos, F., Papadopoulos, G., Mavrakis, N., & Maniadakis, M. (2021). Robotic waste sorting technology: toward a vision-based categorization system for the industrial robotic separation of recyclable waste. IEEE Robotics & Automation Magazine, PP(99), 2-12. Search in Google Scholar

[5] Sun, R., Shi, D., Zhang, Y., Li, R., & Li, R. (2021). Data-driven technology in event-based vision. Complexity, 2021. Search in Google Scholar

[6] Xie, J. F. Y. (2021). Detection of atlantic salmon bone residues using machine vision technology. Food Control, 123(1). Search in Google Scholar

[7] Chen, Y., Ai, H., & Li, S. (2020). Analysis of correlation between carcass and viscera for chicken eviscerating based on machine vision technology. Journal of Food Process Engineering. Search in Google Scholar

[8] Chen, C., & Li, D. (2021). Research on the detection and tracking algorithm of moving object in image based on computer vision technology. Wireless Communications and Mobile Computing, 2021(4), 1-7. Search in Google Scholar

[9] Shen, H., Sun, W., & Fu, J. (2019). Multi-view online vision detection based on robot fused deposit modeling 3d printing technology. Rapid Prototyping Journal, 25(2), 343-355. Search in Google Scholar

[10] Zhang, Zimiao, Shihai, Li, & Qiu. (2017). Efficient vision-based pose determination method of five coplanar points. Journal of Optical Technology. Search in Google Scholar

[11] Chen, S., Duan, H., Deng, Y., Li, C., Zhao, G., & Xu, Y. (2017). Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor. Optical Engineering, 56(12), 1. Search in Google Scholar

[12] Lianhua, H., Chengyi, X., & Feng, Z. (2021). Research on sheepskin contour extraction method based on computer vision measurement technology. Journal of the American Leather Chemists Association(8), 116. Search in Google Scholar

[13] Qiao, L., & Lin, C. W. (2022). Research on standardized feature positioning technology of motion amplitude based on intelligent vision. Mobile networks & applications. Search in Google Scholar

[14] Liu, Z., Zhang, Q., & Lv, H. (2021). Restoration and design of ice sculpture structure following multivision sensor and three-dimensional reconstruction technology. Journal of Sensors(Pt.10), 2021. Search in Google Scholar

[15] Vinod, P. V., Shivam, T., Hebbar, R., & Jha, C. S. (2023). Assessment of trees outside forest (tof) in urban landscape using high-resolution satellite images and deep learning techniques. Journal of the Indian Society of Remote Sensing. Search in Google Scholar

[16] Valencia-Marquez, D., Flores-Tlacuahuac, A., & Aguirre-Soto, A. (2023). Computer aided molecular design coupled to deep learning techniques as a less-expensive approach to design organic photoredox catalysts. Computers and Chemical Engineering, 178. Search in Google Scholar

[17] Estefanie, G. R. N., Seng, N. Y., Weon, J. L., Saleh, S. N., Lehmann, C. U., & Chenlu, T., et al. (2024). Early identification of patients at risk for iron-deficiency anemia using deep learning techniques. American Journal of Clinical Pathology. Search in Google Scholar

[18] Akbar, M. K., Amayri, M., & Bouguila, N. (2024). A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid. Building Simulation, 17(3), 441-457. Search in Google Scholar

[19] Dhiman, H. S., & Deb, D. (2021). Machine intelligent and deep learning techniques for large training data in short-term wind speed and ramp event forecasting. International Transactions on Electrical Energy Systems. Search in Google Scholar

[20] Ilias, L. R. I. (2021). Detecting malicious activity in twitter using deep learning techniques. Applied Soft Computing, 107(1). Search in Google Scholar

[21] Lee, S., & Kim, J. (2021). Predicting inflow rate of the soyang river dam using deep learning techniques. Water. Search in Google Scholar

[22] Li, X., Dong, F., Zhang, S., & Guo, W. (2019). A survey on deep learning techniques in wireless signal recognition. Wireless Communications and Mobile Computing, 2019, 1-12. Search in Google Scholar