Deep spatial-temporal networks for flame detection

Shahid, Mohammad; Chien, I. Chao; Sarapugdi, Wannaporn; Miao, Lili; Hua, Kai-Lung

doi:10.1007/s11042-020-10079-1

Cited by 11 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xie et al [23] used dynamic features and deep image features for recognition. Shahid et al [24] obtained the candidate flame regions by combining the shape features and the motion stroboscopic features of the flame and then used the classifier to identify them. Zhang et al [25] improved the target detection network YOLOv5 by combining the static and dynamic features of the flame, and solved the problem of unbalanced positive and negative samples.…”

Section: B Related Workmentioning

confidence: 99%

A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course

Wang

Zhang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

As an interdisciplinary course, Machine Vision combines AI and digital image processing methods. This paper develops a comprehensive machine vision experiment on forest wildfire detection that organically integrates digital image processing, machine learning and deep learning technologies. Although the research on wildfire detection has made great progress, many experiments are not suitable for students to operate. Also, the detection with high accuracy is still a big challenge. In this paper, we divide the task of forest wildfire detection into two modules, which are wildfire image classification and wildfire region detection. We propose a novel wildfire image classification algorithm based on Reduce-VGGnet, and a wildfire region detection algorithm based on the optimized CNN with the combination of spatial and temporal features. The experimental results show that the proposed Reduce-VGGNet model can reach 91.20% in accuracy, and the optimized CNN model with the combination of spatial and temporal features can reach 97.35% in accuracy. Our framework is a novel way to combine research and teaching. It can achieve good detection performance and can be used as a comprehensive experiment for Machine Vision course, which can provide the support for talent cultivation in machine vision area.

show abstract

Section: B Related Workmentioning

confidence: 99%