A Scalable Deep Learning Framework for Extracting Model Inventory of Roadway Element Intersection Control Types From Panoramic Images

Abstract

In the United States, the Model Inventory of Roadway Element (MIRE) provides a comprehensive list of data that are needed to support states’ data-driven safety programs. The intersection control is part of the MIRE Fundamental Data Elements (FDE) for which state Departments of Transportation are required to complete the collection by September 30, 2026. It is essential roadway data that have been used widely in traffic safety studies. This study proposes a scalable and automated deep learning framework for detecting and classifying stop and yield intersection controls using panoramic street view images. The Faster Region-based Convolutional Neural Networks (Faster R-CNN) model architecture was used to detect and classify stop and yield signs from the images. A transfer learning process was deployed using the Inception-ResNet-v2 generic feature extractor to accelerate the training and performance of the deep learning model with less data collection effort. The effectiveness and scalability of the proposed framework were tested on a sample of road intersections in the state of Michigan. The proposed deep learning model achieved a recall value of 97.7% and 98.2% for detecting and classifying stop and yield signs respectively. The evaluation of the model performance at a county level suggests that the model can be scaled to a statewide level without a substantial increase in the demand for computational resources. As demonstrated in this study, state DOTs can leverage the advancement of deep learning techniques and the availability of imagery data to expedite the process of collecting the MIRE data.

Contrast Limited Adaptive Histogram Equalization for Recognizing Road Marking at Night Based on Yolo Models

“…As a result, the system must adjust the bounding box coordinates in the Yolo input format. The modification process is based on Formula ( 5) - (10). The YOLO architecture is shown in its most basic form in Figure 1(b).…”

“…(4) Low memory requirements: YOLO is designed to operate on a single network, which reduces its memory requirements compared to other object detection systems that require multiple networks. (5) Flexibility: YOLO can be trained on custom datasets and can be fine-tuned for specific applications, making it a versatile algorithm that can adapt to different use cases [9] [10].…”