Fire and Smoke Tracking and Detection in Videos based on Pyramid Convolutional Deep Learning
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 226
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شناسه ملی سند علمی:
ICTI05_046
تاریخ نمایه سازی: 8 آبان 1401
چکیده مقاله:
Nowadays, jungles are burning into fire due to climatechanges. Detection and tracking any smoke will be prevent anyburning, so it needs pattern recognition in images. In thisresearch we propose a developed method for real-time andsynchronous fire and smoke tracking and detection in videos. Inthis approach, at first, we apply a pre-processing phase toenhance the image frames and then deep learning techniquebased on pyramid convolutional neural network apply for datatraining and testing based on fractal model for imagesegmentation and feature extraction. Simulation done inMATLAB platform which results indicated good results in termsof smoke and fire tracking and detection. Also we use someevaluation criteria such as accuracy, sensitivity, specificity, areaunder curve (AUC) with ۹۸.۹۱%, ۹۳.۵۴%, ۹۳.۱۷% and ۰.۸۷۱۹respectively. We use two different image and video dataset in thisresearch and both of them have good performance in terms ofaccuracy in comparison to recent methods.
کلیدواژه ها:
component ، Smoke Detection and Tracking ، Fractal Model ، Pyramid Convolutional Neural Network ، Deep Learning
نویسندگان
Bashir Bagheri Nakhjavanlo
Department of Computer and Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
Monireh Ayari
Department of Computer, Karaj Branch, Islamic Azad University, Karaj, Iran
Nima Aberomand
Department of Computer Engineering, Shahr-e-Qods, Branch, Islamic Azad University, Tehran, Iran -Department of Computer Science, the University of Texas at Arlington, Texas, USA