Enabling YOLOv2 Models to Monitor Fire and Smoke Detection Remotely in Smart Infrastructures

Author:

Saponara Sergio,Elhanashi Abdussalam,Gagliardi Alessio

Publisher

Springer International Publishing

Reference13 articles.

1. Hall, J.R.: The Total Cost of Fire in the United States. National Fire Protection Association, Quincy, MA (2014)

2. Gagliardi, A., Saponara, S.: Distributed video antifire surveillance system based on IoT embedded computing nodes. In: International Conference on Applications in Electronics Pervading Industry, Environment and Society, pp. 405–411. Springer, Cham, September 2019

3. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

4. MathWorks Student Competitions Team. Using Ground Truth for Object Detection (2020). (https://www.mathworks.com/matlabcentral/fileexchange/69180-using-ground-truth-for-object-detection), MATLAB Central File Exchange. Accessed 27 July 2020

5. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and CCD Camera, IEEE Trans. on Instrumentation and Measurement, vol. 54, no. (4) (2005)

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