Detection of Fire Regions from a Video Image Frames in YCbCr Color Model

Singh*, Yumnam Kirani; Deb, Debasish

doi:10.35940/ijrte.c5637.098319

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…F1-score = 2 × Precision × Recall Precision + Recall (10) where TP (True Positive) is the correct detection of fire, FN (False Negative) is not detected from the fire image, FP (False Positive) is the number of false detections of non-fire objects.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5

Hai-qing

Fang

et al. 2023

Fire

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One of the major responsibilities for forest police is forest fire prevention and forecasting; therefore, accurate and timely fire detection is of great importance and significance. We compared several deep learning networks based on the You Only Look Once (YOLO) framework to detect forest flames with the help of unmanned aerial vehicle (UAV) imagery. We used the open datasets of the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) to train the YOLOv5 and its sub-versions, together with YOLOv3 and YOLOv4, under equal conditions. The results show that the YOLOv5n model can achieve a detection speed of 1.4 ms per frame, which is higher than that of all the other models. Furthermore, the algorithm achieves an average accuracy of 91.4%. Although this value is slightly lower than that of YOLOv5s, it achieves a trade-off between high accuracy and real-time. YOLOv5n achieved a good flame detection effect in the different forest scenes we set. It can detect small target flames on the ground, it can detect fires obscured by trees or disturbed by the environment (such as smoke), and it can also accurately distinguish targets that are similar to flames. Our future work will focus on improving the YOLOv5n model so that it can be deployed directly on UAV for truly real-time and high-precision forest flame detection. Our study provides a new solution to the early prevention of forest fires at small scales, helping forest police make timely and correct decisions.

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Section: Evaluation Metricsmentioning

confidence: 99%

Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5

Hai-qing

Fang

et al. 2023

Fire

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“…Visual-based technologies provide an economical solution for extensive and all-climate fire surveillance in open-space scenarios by detecting features of flame and smoke [9][10][11][12]. Researchers have transformed these images into alternate color spaces to extract color characteristics and identify flames according to a preset threshold [13,14]. Additionally, edges, textures, and other features have also been used for fire detection [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An Image-Based Fire Monitoring Algorithm Resistant to Fire-like Objects

Xu,

Zhang,

Deng

et al. 2023

Fire

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Due to its wide monitoring range and low cost, visual-based fire detection technology is commonly used for fire detection in open spaces. However, traditional fire detection algorithms have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. These algorithms have poor anti-interference ability against fire-like objects, such as emissions from factory chimneys, clouds, etc. In this study, we developed a fire detection approach based on an improved YOLOv5 algorithm and a fire detection dataset with fire-like objects. We added three Convolutional Block Attention Modules (CBAMs) to the head network of YOLOv5 to improve its feature extraction ability. Meanwhile, we used the C2f module to replace the original C2 module to capture rich gradient flow information. Our experimental results show that the proposed algorithm achieved a mAP@50 of 82.36% for fire detection. In addition, we also conducted a comparison test between datasets with and without labeling information for fire-like objects. Our results show that labeling information significantly reduced the false-positive detection proportion of fire-like objects incorrectly detected as fire objects. Our experimental results show that the CBAM and C2f modules enhanced the network’s feature extraction ability to differentiate fire objects from fire-like objects. Hence, our approach has the potential to improve fire detection accuracy, reduce false alarms, and be more cost-effective than traditional fire detection methods. This method can be applied to camera monitoring systems for automatic fire detection with resistance to fire-like objects.

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Detection and Counting of Connected Lentil Grains Using Convex Deficiency for Quality Estimation

Singh

Akuli

2023

Lecture Notes in Networks and Systems

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Detection of Fire Regions from a Video Image Frames in YCbCr Color Model

Cited by 3 publications

References 8 publications

Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5

Forest Flame Detection in Unmanned Aerial Vehicle Imagery Based on YOLOv5

An Image-Based Fire Monitoring Algorithm Resistant to Fire-like Objects

Detection and Counting of Connected Lentil Grains Using Convex Deficiency for Quality Estimation

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