Author:
Ali Abd-elmegeid Amin,Ali Iman jebur,Hassan Hassan Shaban
Abstract
In this paper, we propose a video-based model for fire detection using a model designed to detect fire and smoke after video processing. Then, the model was developed by increasing the rate of fire detection in a single image and using a pre-trained model. The real-time detection procedure is verified in 0.1 second. Also, an AI technique has been created to detect smoke and fire using deep learning (Effective Network). This is a more stable and faster technology than the current technologies in use. Like VGG16, VGG19, ResNet and the comparison was made with ResNet because it is better than other techniques. The results indicated that the proposed technique was better than ResNet.
Reference16 articles.
1. BOCHKOVSKIY, Alexey; WANG, Chien-Yao; LIAO, Hong-Yuan Mark. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020.
2. VEERAMANI, Balaji; RAYMOND, John W.; CHANDA, Pritam. DeepSort: deep convolutional networks for sorting haploid maize seeds. BMC bioinformatics, 2018, 19.9: 1-9.
3. FELZENSZWALB, Pedro; MCALLESTER, David; RAMANAN, Deva. A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE conference on computer vision and pattern recognition. Ieee, 2008. p. 1-8.
4. XIONG, Huan, et al. On the number of linear regions of convolutional neural networks. In: International Conference on Machine Learning. PMLR, 2020. p. 10514-10523.
5. ALBAWI, Saad; MOHAMMED, Tareq Abed; AL-ZAWI, Saad. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). Ieee, 2017. p. 1-6.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献