Analysis of Deep Learning Methods for Early Wildfire Detection Systems: Review

Mahdi, Ahmed Saleem; Mahmood, Sawsen Abdulhadi

doi:10.1109/iiceta54559.2022.9888515

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…For object detection, a neural network is utilized in a single pass, directly predicting bounding boxes and class probabilities. YOLO's holistic approach considers the entire image, enabling it to capture context and make accurate predictions [35], [36].…”

Section: B Yolov5 Deep Network Modelmentioning

confidence: 99%

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Jabbar Hussein,

Hamood Al-Saadi

2024

IJCDS

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Automated analysis of retinal images is a crucial diagnostic method for the early detection of disorders affecting the eyes, such as glaucoma, diabetic macula edema (DME), and retinopathy brought on by diabetes. The current study introduces a reliable technique for segmenting and detecting optic discs using a deep learning-based technique. This is comparable to the initial stage of creating a diagnostic system supported by a computer for diabetic macula edema in retinal images. The suggested approach is predicated on the recommended approach using the YOLO (You Only Look Once) algorithm for detection objects and segmentation for bounding boxes that belong to the same category, comparing the Intersection over Union (IOU) values of each bounding box with those of the others. If IOU values are higher than thresholds, they will consider them the same targets and maintain the boundary boxes with the highest reliability. Three retinal image databases that are accessible to the public are used to quantitatively assess the technique: Messidor-1, Messidor-2, and the IDRID Database. The technique yields a success rate of 99.5% for optic disc identification, a precision of 99.9%, and a recall of 100% for Messidor-1, and testing for all Messidor-2 and IDRID databases accepts accuracy of 99.1% and 98.7% respectively. When it comes to the identification and border extraction of the optic disc, this special technique has demonstrated a notable improvement over the previous approach.

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Section: B Yolov5 Deep Network Modelmentioning

confidence: 99%

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Jabbar Hussein,

Hamood Al-Saadi

2024

IJCDS

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“…Nowadays, the research community has high expectations in the use of artificial intelligence in the field of wildfire detection and many researchers have proposed different algorithms and methodologies to effectively and timely detect wildfire occurrence. Mahdi et al [ 98 ] categorized machine learning approaches for fire detection into two main groups: traditional methods and deep learning methods. Traditional methods encompass classical algorithms such as decision trees and support vector machines (SVM), while deep learning methods represent the prevailing models that include various types of artificial neural networks.…”

Section: The Role Of Traditional Machine Learning and Deep Learning I...mentioning

confidence: 99%

Advancements in Forest Fire Prevention: A Comprehensive Survey

Carta,

Zidda,

Putzu

et al. 2023

Sensors

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Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires.

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“…This demonstrated the algorithm's flexibility in detecting real-world scenes and had important practical implications. This work not only produced a practical concept for feature extraction and fusion of YOLOv5, but it also opened the door for the use of firesmoke detection in forest and indoor scenarios [13].…”

Section: Zhang Et Al Shows a Brand-new Model Of An Algorithmmentioning

confidence: 99%

Wildfire Detection System using Yolov5 Deep Learning Model

Saleem,

Abdulhadi

2023

IJCDS

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Wildfires pose a significant threat to the natural environment as well as public safety.Forest fire detection is critical for effective fighting, as once a wildfire has grown to a certain size, it is difficult to control. Recently, there has been a growing demand for forest areas to install a rapid response system to allow for prompt and timely action in the event of forest fires expanding across large areas. In this paper, a proposed framework for wildfire detection in a video sequence using the YOLOv5 deep learning model is presented and implemented. The interested regions represented by (fire object) in the video sequence are extracted using a new auto-annotation scheme to determine the ROI (Region of Interest) based on the edge detection process. Since the public wildfire datasets are yet confined, therefore we have constructed a new wildfire dataset named WILDFIRE-I dataset composed of variant fire images to conduct the performance evaluation of the proposed system. A comparison study with state of art research was performed in our experiments to demonstrate the efficiency of the proposed system based on common performance evaluation metrics. The experimental results exhibited detection accuracy of fire events close to 98 %, with a manual annotation process, while the proposed annotation process has achieved an accuracy of 96 %, with minimum time processing required for dataset image labelling.

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Analysis of Deep Learning Methods for Early Wildfire Detection Systems: Review

Cited by 11 publications

References 29 publications

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

The Optic Disc Detection and Segmentation in Retinal Fundus Images Utilizing You Only Look Once (YOLO) Method

Advancements in Forest Fire Prevention: A Comprehensive Survey

Wildfire Detection System using Yolov5 Deep Learning Model

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