چکیده مقاله
Computer vision is a pivotal technology of the current decade, enabling computers to interpret visual inputs, extract information, and make informed decisions Core challenges in computer vision encompass detection, segmentation, and classification, with detection being crucial for applications like transportation systems However, adverse weather conditions, such as rain, snow, and fog, pose significant challenges for object detection systems in real world environments Traditional approaches often incorporate denoising or enhancement modules to preprocess images An alternative strategy involves training models using a mix of real and synthetic data This method not only reduces data processing costs but also enhances model adaptability to various conditions This paper focuses on enhancing the robustness and accuracy of car detection models under adverse weather conditions using the YOLO You Only Look Once object detection model By leveraging generative AI to create synthetic weather condition data based on real images captured from city surveillance cameras, this paper developed a comprehensive dataset for training Our contributions include a generative AI pipeline to simulate various weather scenarios and the integration of this synthetic data with real data to train the YOLO model The results demonstrate improved performance and reliability of car detection systems in challenging environments, highlighting the efficacy of combining synthetic and real data for robust computer vision applications
کلیدواژهها
نویسندگان
Sina Khoshgoftar
Industrial and Systems Engineering, Tarbiat Modares, Tehran, Iran
Mehrdad Kargari
Associate Professor at the Faculty of Industrial and Systems Engineering, Tarbiat Modares, Tehran, Iran
Reza Vatankhah Barenji
Senior Lecturer, School of Science and Technology, Nottingham Trent, Nottingham, UK
شیوه ارجاع
Khoshgoftar, Sina and Kargari, Mehrdad and Vatankhah Barenji, Reza,1403,Generative AI Strategies to Enhance Car Detection Under Adverse Weather Conditions,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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