Smart Automation Through Computer Vision and Image Processing: A Deep Learning Approach
Osita Miracle Nwakeze, Naveed Uddin Mohammed, Obaze Caleb Akachukwu,
Christopher Odeh, Ezekiel-Odimgbe Chinenye Love
1Department of Computer Science, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State,
2Department of Computer Science, Lindsey Wilson University, Columbia, Kentucky, USA,
4Department of Computer Engineering, Nnamdi Azikiwe University, Awka, Anambra State Nigeria,
3 5Department of Computer science, Dennis Osadebay University Asaba, Delta State, Nigeria.
Abstract
The Artificial Intelligence (AI) area has been undergoing an explosive growth in the past years and computer vision and image processing seem to be the most promising technologies to enable such a process. This paper will give an overview of the newest trends that fuse these technologies along with the deep learning to enable intelligent, adaptive and autonomous systems in industries, healthcare, agriculture and urban applications. The review is a systematic literature search of peer-reviewed articles from the year 2013 to 2025 and it is available from IEEE Xplore, ScienceDirect, SpringerLink and MDPI databases.
Keywords
Computer Vision, Image Processing, Smart Automation, Deep Learning, Industrial Intelligence, Artificial Intelligence.
References
[1] Addula, S. R., & Tyagi, A. K. (2024). Future of computer vision and industrial robotics in smart manufacturing. In A. K. Tyagi, S. Tiwari, S. K. Arumugam, & A. K. Sharma (Eds.), Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing (Chapter 22). Wiley. https://doi.org/10.1002/9781394303601.ch22
[2] Ahmed, S., Khan, M., & Raza, B. (2022). Edge computing for real-time image processing in IoT systems. IEEE Internet of Things Journal, 9(4), 3102–3115. https://doi.org/10.1109/JIOT.2022.3145678
[3] Bai, Y., Chen, Z., & Liu, H. (2023). Deep learning-based traffic flow prediction for smart cities. ACM Transactions on Intelligent Systems and Technology, 14(1), 1–20. https://doi.org/10.1145/3591234
[4] Bhana, R., Mahmoud, H., & Idrissi, M. (2024). Smart industrial safety using computer vision. School of Computing and Digital Technology, Birmingham City University. Presented under the Knowledge Transfer Partnership project (KTP011657) with Conway Packing Services.
[5] Cedergren, J., & Berglund, J. (2021). Real-time computer vision in industrial automation (Master’s thesis, Lund University). Department of Automatic Control, Lund University.
[6] Che, C., Zheng, H., Huang, Z., Jiang, W., & Liu, B. (2024). Intelligent robotic control system based on computer vision technology. arXiv preprint arXiv:2404.01116. https://doi.org/10.48550/arXiv.2404.01116
[7] Chen, Y., Wang, H., & Liu, Z. (2023). Deep learning for medical image segmentation: A review. Information Fusion, 91, 123–135. https://doi.org/10.1016/j.inffus.2023.102123
[8] Cheng, Q. (2021). Application of computer vision technology in industrial automation. Journal of Physics: Conference Series, 2037, 012015. https://doi.org/10.1088/1742-6596/2037/1/012015
[9] Cheng, Y.-P., Li, C.-W., & Chen, Y.-C. (2019). Apply computer vision in GUI automation for industrial applications. Mathematical Biosciences and Engineering, 16(6), 7526–7545. https://doi.org/10.3934/mbe.2019378
[10] Cui, C., & Li, Y. (2021). Application of computer visual recognition in industrial automation assembly production line. Journal of Physics: Conference Series, 1992(3), 032067. https://doi.org/10.1088/1742-6596/1992/3/032067
[11] Fernández-Caramés, T. M., & Fraga-Lamas, P. (2022). Computer vision in environmental monitoring: A review. Sensors, 22(5), 1987. https://doi.org/10.3390/s22051987
[12] Gao, J., Li, Y., & Sun, H. (2023). UAV-based crop monitoring using deep learning. Scientific Reports, 13, 11234. https://doi.org/10.1038/s41598-023-31234-5
[13] Gupta, R., Sharma, A., & Mehta, P. (2023). Vision-based robotic inspection for manufacturing automation. Journal of Intelligent Manufacturing, 34(2), 567–580. https://doi.org/10.1007/s10845-023-02012-7
[14] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90
[15] He, K., Zhang, X., Ren, S., & Sun, J. (2022). Deep residual learning for image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1), 123–135. https://doi.org/10.1109/TPAMI.2022.3144567
[16] Hossain, M. S., Muhammad, G., & Song, B. (2023). Vision-based safety monitoring in industrial environments. IEEE Transactions on Industrial Informatics, 19(2), 1234–1245. https://doi.org/10.1109/TII.2023.3145679
[17] Jain, T., & Meenu. (2013). Automation and integration of industries through computer vision systems. International Journal of Information and Computation Technology, 3(9), 963–970. Retrieved from International Research Publications House.
[18] Khan, S., Naseer, M., Hayat, M., Zamir, S. W., & Shah, M. (2022). Transformers in vision: A survey. ACM Computing Surveys, 55(12), 1–41. https://doi.org/10.1145/3505244
[19] Kumar, V., Singh, R., & Yadav, A. (2022). Smart automation using computer vision in agriculture. Sensors, 22(18), 6789. https://doi.org/10.3390/s22186789
[20] Kumar, V., Singh, R., & Yadav, A. (2023). Digital twin integration with computer vision for predictive maintenance. Machines, 11(1), 45. https://doi.org/10.3390/machines11010045
[21] Li, X., Zhang, Y., & Wang, J. (2022). Real-time defect detection using YOLOv5 in manufacturing. Robotics and Computer-Integrated Manufacturing, 78, 102345. https://doi.org/10.1016/j.rcim.2022.102345
[22] Liu, Y., Chen, X., & Zhang, T. (2023). Facial recognition using hybrid deep learning models. Neurocomputing, 512, 45–58. https://doi.org/10.1016/j.neucom.2023.01.045
[23] More, S. J., & More, A. J. (2024). Computer vision & IoT based smart conveyor belt sorting system. International Journal of Engineering Research & Technology (IJERT), 13(6), Article IJERTV13IS060066. http://www.ijert.org
[24] Nguyen, T., Pham, H., & Tran, D. (2023). Vision-based automation for smart city infrastructure. Urban Informatics, 2(1), 45–60. https://doi.org/10.1007/s44212-023-00045-1
[25] Nwakeze, O. M., & Mohammed, N. U. (2025). Development of a deep learning-based framework for security of IoT network protocol. International Journal of Innovative and Applied Research, 13(7), 16–26. https://doi.org/10.58538/IJIAR/2136
[26] Patel, D., Sharma, K., & Mehta, R. (2022). Deep learning in precision agriculture: A review. Computers and Electronics in Agriculture, 198, 107123. https://doi.org/10.1016/j.compag.2022.107123
[27] Rahman, M. A., Shahrior, M. F., Iqbal, K., & Abushaiba, A. A. (2025). Enabling intelligent industrial automation: A review of machine learning applications with digital twin and edge AI integration. Automation, 6(3), 37. https://doi.org/10.3390/automation6030037
[28] Rajpurkar, P., Irvin, J., & Zhu, K. (2022). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. Scientific Reports, 12, 12345. https://doi.org/10.1038/s41598-022-12345-6
[29] Rani, S., Kaur, H., & Singh, J. (2023). Image enhancement using histogram equalization and morphological filters. Electronics, 12(3), 456. https://doi.org/10.3390/electronics12030456
[30] Singh, A., & Sharma, M. (2023). Vision-guided robotic surgery: A review. Health and Technology, 13(2), 234–248. https://doi.org/10
[31] Singh, A., & Verma, R. (2022). A comparative study of edge detection techniques in image processing. Journal of Visual Communication and Image Representation, 83, 103456. https://doi.org/10.1007/s00371-022-02567-9
[32] Singh, P., Kumar, N., & Sharma, D. (2023). Computer vision applications in smart city surveillance. Scientific Reports, 13, 11234. https://doi.org/10.1038/s41598-023-31234-5
[33] Szeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). Springer. https://doi.org/10.1007/978-3-030-34372-9
[34] Wang, J., Li, H., & Zhao, Y. (2022). Gesture recognition using CNN and LSTM networks. IEEE Transactions on Human-Machine Systems, 52(3), 234–245. https://doi.org/10.1109/THMS.2022.3145679
[35] Yousif, I., Burns, L., ElKalach, F., & Harik, R. (2024). Leveraging computer vision towards high-efficiency autonomous industrial facilities. Journal of Intelligent Manufacturing, 36, 2983–3008. https://doi.org/10.1007/s10845-024-02396-1
[36] Zhang, L., & Li, X. (2022). Traffic monitoring using deep learning and computer vision. ACM Transactions on Intelligent Systems and Technology, 13(4), 1–20. https://doi.org/10.1145/3501234
[37] Zhang, Y., Chen, M., & Zhou, Q. (2023). Semantic segmentation for urban scene understanding. Pattern Recognition Letters, 165, 45–56. https://doi.org/10.1016/j.patrec.2023.01.012
[38] Zhao, X., Huang, Y., & Lin, Z. (2023). Object detection using YOLOv5 and transformer-based models. Applied Sciences, 13(2), 2345. https://doi.org/10.3390/app13022345
[39] Zhou, H. A., Wolfschläger, D., Florides, C., Werheid, J., Behnen, H., Woltersmann, J.-H., Pinto, T. C., Abdelrazeq, A., Schmitt, R. H., & Kemmerling, M. (2025). Generative AI in industrial machine vision: A review. Journal of Intelligent Manufacturing, 36, Article 02604. https://doi.org/10.1007/s10845-025-02604-6