Naeem Raza1, Muhammad Asif Habib1, Mudassar Ahmad1, Qaisar Abbas2,*, Mutlaq B. Aldajani2, Muhammad Ahsan Latif3
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 911-931, 2024, DOI:10.32604/cmc.2024.055049
- 15 October 2024
Abstract Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle… More >
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