AI and Machine Learning automation for Fiber optic infrastructure Enhancement

Open Fiber aims to deliver the fastest internet services nationwide, ensuring that every home can access high-speed fiber optic connectivity. Investing in cutting-edge infrastructure and expanding network reach, the organization is committed to bridging the digital divide and fostering innovation in urban and rural areas. However, due to a lack of actual records, Open Fiber faces challenges in accurately estimating residential buildings in specific regions, delaying infrastructure deployment timelines. Additionally, relying on third parties for manual surveys is time-consuming and costly. At the same time, digital records for small towns and rural areas are insufficient or inaccurate for estimating the number of residential houses. To address these challenges, we propose an AI/ML-based method that automates the collection of house numbers from street view data, along with their corresponding geo-coordinates. Our approach expedites the optical fiber infrastructure mapping process, enhances deployment efficiency in rural and urban areas, manages the digital record, and decreases overall costs. This technique can facilitate organizations and enable them to make data-driven decisions for effective fiber optic infrastructure planning and management. The proposed methodology employs advanced deep learning models for object detection and text recognition in a complex street view environment, including YOLOv8, Yolov8-AM Faster R-CNN, SSD, and Detectron2, tailored for detecting house number plates under diverse real-world conditions. A custom dataset is manually collected and annotated to ensure comprehensive training coverage to achieve our objectives. For digit recognition, we employ models such as ResNet, ViT+LSTM, Yolov8-AM DETR, fine-tuned PaddleOCR, and EasyOCR, which are employed in a post-processing phase, enhancing accuracy through integration with OCR frameworks. The YOLOv8-AM model achieved top detection performance with 95% accuracy, 92% precision, and an mAP@0.5 of 0.926, surpassing YOLOv8, Faster R-CNN, and Detectron2. For recognition, YOLOv8-AM also led with a precision of 91% and recall of 93%, outperforming ViT+LSTM and ResNet. The research also includes developing an application built on the deployed models. This application is designed to visually present detected houses and their spatial coordinates to estimate the distance from the nearest hub. This app integrates detection and recognition outputs, providing a mapped visualization of identified house numbers and their geographic locations. The application demonstrates how these models will work in real world grounds, reducing manual processes and supporting precise, efficient infrastructure expansion and management.