Detecting Traffic Flow Parameters in Snow
Author Information
Author(s): Jian Cheng, Xie Tiancheng, Hu Xiaojian
Primary Institution: Southeast University, Nanjing, China
Hypothesis
Can a deep learning framework effectively extract traffic flow parameters from videos during snowfall?
Conclusion
The proposed framework can accurately detect traffic flow parameters from snowy images, achieving a 97.2% accuracy under moderate snow conditions.
Supporting Evidence
- The framework improves vehicle recognition accuracy by 8.6% after snow removal.
- Traffic flow parameter estimation accuracy reaches 97.2% under moderate snow conditions.
- The proposed method outperforms traditional traffic monitoring systems.
Takeaway
This study shows how to use smart computer programs to see cars and measure traffic even when it's snowing.
Methodology
The framework includes four stages: snow particle removal, snow streak removal, vehicle detection using yolov5, and traffic flow parameter estimation using the virtual coil method.
Limitations
The model's accuracy decreases with heavier snowfall due to increased snow density.
Digital Object Identifier (DOI)
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