Telecommunication Fiber Box Detection Using YOLO in Urban Environment

Azib-Jazman Azmawi, Wan-Noorshahida Mohd-Isa, Abdul Aziz Abdul Rahman


The Fiber Distribution Panel (FDP) box is an essential piece of internet access hardware because it provides users with high-speed data networking and functions as a cable organizer to reduce wire clutter. After installing the FDP, an inspection must be performed to ensure that all necessary components are present. However, This examination is still done manually; the technician snaps a picture of the panel and sends it to its supervisor for verification, which is time-consuming and often prone to errors. In addition to images captured in low-light and complex environments, it makes it more difficult for humans to identify the components with just a naked eye. On this matter, a much more efficient method to assess the FDP installation work is very much needed. Therefore, using computer vision approaches, we utilize a deep learning algorithm to perform object detection and automate the assessment of FDP installation components based on visual data. One of the deep learning models established in the literature is the You Only Look Once (YOLO) model, a one-stage deep learning object detection algorithm that employs a fully conventional approach to generate highly accurate real-time predictions. This paper uses YOLOv5s to identify the fiber box and its relevant components, even in urban environments. Experimentations show that YOLO successfully identified the installation parts with a mean average precision score of 86% at a 0.5 confidence level, even with limited data.


Fiber distribution panel; computer vision; deep learning; optics network; complex background

Full Text:



Wickramasinghe, S. R., & Razak, K. A. (2023). The Impact Of The Telecommunication Industry As AModerator on Poverty Alleviation and Educational Programmes To Achieve Sustainable Development Goals In Developing Countries. Journal of Informatics and Web Engineering, 2(1), 25-37. doi:

Veligura, N., Chan, K. K.-K., Ingen, F. v., & Cufre, G. (2020, May). COVID-19’s Impact on the Global Telecommunications Industry. p. 6.

Department of Statistics Malaysia (DOSM). (2023). ICT Use and Access By Individuals And Households Survey Report 2022. Putrajaya: Department of Statistics Malaysia.

Neves, A. (2021, November 17). Basics to Help You Know Everything About Fiber Distribution Box. Retrieved from twoosk:

Sheldon. (2019, November 21). Fiber Distribution Panel Wiki, Types and Buying Tips. Retrieved from FS:

Du, L., Zhang, R., & Wang, X. (2020, May). Overview of two-stage object detection algorithms. In Journal of Physics: Conference Series (Vol. 1544, No. 1, p. 012033). IOP Publishing.

Zhiqiang, W., & Jun, L. (2017). A Review of Object Detection Based on Convolutional Neural Network. 2017 36th Chinese Control Conference (CCC) (pp. 11104-11109). IEEE.

Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243-9275. doi:

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 779-788).

Terven, J. R., & Cordova-Esparaza, D. M. (2023). A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond. 1-27. doi:

Olorunshola, O. E., Irhebhude, M. E., & Evwiekpaefe, A. E. (2023). A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms. Journal of Computing and Social Informatics, 2(1), 1-12. doi:

Munawar, M. R. (2022, 12 4). FAQs on YOLOv5 and YOLOv7. Retrieved from medium:

Guo, B., Zou, Y., Fang, Y., Goh, Y. M., & Zou, P. X. (2021). Computer vision technologies for safety science and management in construction: A critical review and future research directions. Safety Science, 135, 1-37. doi:

Paneru, S., & Jeelani, I. (2021). Computer vision applications in construction: Current state, opportunities & challenges. Automation in Construction, 132, 1-17. doi:

Kolar, Z., Chen, H., & Luo, X. (2018). Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Automation in Construction, 89, 58-70. doi:

Fang, W., Zhong, B., Zhao, N., Love, P. E., Luo, H., Xue, J., & Xu, S. (2019). A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network. Advanced Engineering Informatics, 39, 170-177. doi:

Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91-99.

Anushkannan, N. K., Kumbhar, V. R., Maddila, S. K., & Kolli, C. S. (2022). YOLO Algorithm for Helmet Detection in Industries for Safety Purpose. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), (pp. 225-230).

Dong, X., Yan, S., & Duan, C. (2022). A lightweight vehicles detection network model based on YOLOv5. Engineering Applications of Artificial Intelligence, 113, 1-14. doi:

Wang, C.-Y., Liao, H.-Y. M., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., & Hsieh, J.-W. (2020). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, (pp. 390-391). doi:

Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021). A Forest Fire Detection System Based on Ensemble Learning. Forests, 12(217), 1-17. doi:

Fang, C., Xiang, H., Leng, C., Chen, J., & Yu, Q. (2022). Research on Real-Time Detection of Safety Harness Wearing of Workshop Personnel Based on YOLOv5 and OpenPose. Sustainability, 14(10), 1-18. doi:

Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 8759-8768). doi:

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, (pp. 658-666). doi:



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development