Jian Cheng, Xie Tiancheng, Hu Xiaojian, Lu Jian
Nanjing LES Information Technology Co., Ltd., Nanjing 211189, China.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.
J Imaging. 2024 Nov 22;10(12):301. doi: 10.3390/jimaging10120301.
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions. The framework encompasses four distinct stages aimed at addressing the challenges posed by image degradation and the diminished accuracy of traffic flow parameter recognition caused by snowfall. The initial two stages propose a deep learning network for removing snow particles and snow streaks, resulting in an 8.6% enhancement in vehicle recognition accuracy after snow removal, specifically under moderate snow conditions. Additionally, the operation speed is significantly enhanced. Subsequently, the latter two stages encompass yolov5-based vehicle recognition and the employment of the virtual coil method for traffic flow parameter estimation. Following rigorous testing, the accuracy of traffic flow parameter estimation reaches 97.2% under moderate snow conditions.
近年来，计算机视觉技术的进步为智能交通应用带来了新的前景，特别是在自动交通流数据收集领域。在这一新兴趋势中，对于许多未来应用而言，能够快速、准确地检测车辆并从降雪条件下拍摄的视频中提取交通流参数变得至关重要。本文提出了一个新的分析框架，旨在从降雪条件下记录的交通流视频中提取交通流参数。该框架包括四个不同阶段，旨在应对图像退化以及降雪导致的交通流参数识别准确性降低所带来的挑战。前两个阶段提出了一个深度学习网络，用于去除雪粒子和雪条纹，在除雪后车辆识别准确率提高了8.6%，特别是在中度降雪条件下。此外，运行速度显著提高。随后，后两个阶段包括基于yolov5的车辆识别以及采用虚拟线圈方法进行交通流参数估计。经过严格测试，在中度降雪条件下，交通流参数估计的准确率达到97.2%。
