A Benchmark Dataset and YOLO-Based Evaluation Framework for Vehicle Detection Before, During, and After Heavy Snow

Heavy snow substantially degrades the quality and reliability of vision-based traffic monitoring, yet most existing perception benchmarks focus on onboard driving datasets, mixed adverse-weather settings, or broad surveillance scenarios rather than fixed roadside CCTV imagery collected before, during, and after a snow event. This project develops a public benchmark dataset and evaluation framework for vehicle detection using roadside CCTV camera imagery collected before, during, and after heavy snow events, along with an improved YOLO-based vehicle detection method to handle heavy snow conditions. The study targets a sensing setting that is highly relevant to transportation agencies but underrepresented in existing benchmarks: medium-resolution, street-level roadside cameras deployed for routine mobility, safety surveillance, and operational monitoring. It will synthesize the literature on adverse-weather detection, curate and manually annotate a fixed-camera dataset, develop YOLO-based detection pipelines, and rigorously compare out-of-the-box and retrained models across the three snow phases. The project is expected to produce four primary outputs. First, a publicly available benchmark dataset of manually labeled roadside CCTV images spanning before-, during-, and after-snow conditions, drawn from open-source Maryland DOT traffic cameras and annotated with bounding boxes, class labels, and metadata such as timestamp, camera ID, and snow phase. Second, a rigorous benchmark evaluation quantifying how heavy snow and residual post-snow scene changes affect detection performance, compared across standard metrics including precision, recall, F1-score, and mAP. Third, a retrained YOLO-based model, incorporating snow-specific augmentation, phase-balanced sampling, and domain-adaptive fine-tuning, that improves robustness relative to the default baseline. Fourth, an open evaluation protocol and trained model weights released to support reproducible research.

• Record URL:
  • https://smartercenter.org/about-2/projects/a-benchmark-dataset-and-yolo-based-evaluation-framework-for-vehicle-detection-before-during-and-after-heavy-snow/

Language

• English

Project

• Status: Active
• Funding: $160,000.00
• Contract Numbers:

  69A3552348303

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Performing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Principal Investigators:

  Jeihani, Mansoureh

  Yang, Di

• Start Date: 20260801
• Expected Completion Date: 20280301
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Cameras; Computer vision; Image analysis; Snow; Vehicle detectors
• Identifier Terms: YOLO
• Geographic Terms: Maryland
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01998037
• Record Type: Research project
• Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
• Contract Numbers: 69A3552348303
• Files: UTC, RIP
• Created Date: Aug 1 2026 10:40AM