Automated Object Counting System in Digital Images


similar objects

How to Cite

Rivas, M., & Lobo, E. (2023). Automated Object Counting System in Digital Images. Minerva, 4(12), 9-20.


In inventories, the context of object counting and speed of response becomes essential. The process is more straightforward when there are only a few items, but its complexity lies when there are many close or overlapping items. In addition, the pressure to meet tight inventory deadlines adds another challenge. This work aimed to develop a program that automates the accurate counting of similar objects in digital images, regardless of their quantity or arrangement. For this purpose, an algorithm has been implemented in Python. The main results show efficiency in analyzing images with similar objects, a significant step towards separating and accurately counting adjacent objects in various scientific fields. This solution promises to simplify and speed up the counting process in digital images, with potential beneficial applications in multiple scientific disciplines.


[1] P. Li, J. Zheng, P. Li, H. Long, M. Li, and L. Gao, “Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8,” Sensors, vol. 23, no. 15, 2023, doi: 10.3390/s23156701.
[2] P. Jasitha and P. N. Pournami, “Glomeruli Detection Using Faster R-CNN and CenterNet,” in 2023 3rd Asian Conference on Innovation in Technology, ASIANCON 2023, 2023. doi: 10.1109/ASIANCON58793.2023.10270511.
[3] A. Aharari, K. Kuwaduru, and F. Mehdipour, “Development of an Artificial Intelligence (AI) Based Visual Counting System for the Food Industry,” in 13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023, 2023, pp. 136–139. doi: 10.1109/ISCAIE57739.2023.10165399.
[4] S. K. Aruna, N. Deepa, and T. Devi, “Underwater Fish Identification in Real-Time using Convolutional Neural Network,” in Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023, 2023, pp. 586–591. doi: 10.1109/ICICCS56967.2023.10142531.
[5] E. Carboni et al., “A Workflow for the Performance of the Differential Ovarian Follicle Count Using Deep Neuronal Networks,” Toxicol Pathol, vol. 49, no. 4, pp. 843–850, 2021, doi: 10.1177/0192623320969130.
[6] K. M. Spoorthy, S. G. Hegde, N. Vijetha, M. S. Rudramurthy, T. G. Keerthan Kumar, and S. A. Sushma, “Performance analysis of bird counting techniques using digital photograph,” in Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021, 2021, pp. 1482–1491. doi: 10.1109/ICICCS51141.2021.9432265.
[7] T. De Cesaro Júnior and R. Rieder, “Automatic identification of insects from digital images: A survey,” Comput Electron Agric, vol. 178, 2020, doi: 10.1016/j.compag.2020.105784.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Download data is not yet available.