Early Wildfire Smoke Detection Using Different YOLO Models

Al-Smadi, Yazan; Alauthman, Mohammad; Al-Qerem, Ahmad; Aldweesh, Amjad; Quaddoura, Ruzayn; Aburub, Faisal; Mansour, Khalid; Alhmiedat, Tareq

doi:10.3390/machines11020246

Cited by 33 publications

(18 citation statements)

References 23 publications

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“…Mean average precision ( mAP ) is calculated by combining IoU , precision, recall, and the confusion matrix, making it one of the most crucial metrics in object detection [ 26 ]. It represents the average precision across multiple classes, with values ranging from [0, 1], where higher values indicate better performance.…”

Section: Description Of the Problemmentioning

confidence: 99%

G-YOLO: A YOLOv7-based target detection algorithm for lightweight hazardous chemical vehicles

Yu,

Zhou,

Liu

et al. 2024

PLoS ONE

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Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.The method first introduces a lightweight feature extraction structure, E-GhostV2 network, into the trunk and neck of the model to achieve effective feature extraction while reducing the burden of the model. Additionally, the PConv is used in the model’s backbone to effectively reduce redundant computations and memory access, thereby enhancing efficiency and feature extraction capabilities. Furthermore, to address the problem of performance degradation caused by overemphasizing high-quality samples, the model adopts the WIoU loss function, which balances the training effect of high-quality and low-quality samples, enhancing the model’s robustness and generalization performance. Experimental results demonstrate that the improved model achieves satisfactory detection accuracy while reducing the number of model parameters, providing robust support for theoretical research and practical applications in the field of hazardous chemical vehicle object detection.

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Section: Description Of the Problemmentioning

confidence: 99%

G-YOLO: A YOLOv7-based target detection algorithm for lightweight hazardous chemical vehicles

Yu,

Zhou,

Liu

et al. 2024

PLoS ONE

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“…In addition, one of the biggest problems in object detection is different weather conditions or low model performance in some situations (sun reflection, lack of light, etc.) [ 21 ]. Therefore, it is significant to apply data augmentation techniques to expand the dataset for model training [ 22 , 23 , 24 , 25 ].…”

Section: Proposed Workmentioning

confidence: 99%

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Avazov

Jamil

Muminov

et al. 2023

Sensors

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Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

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“…Interest within the academic area has been directed toward the resolution of early detection, entailing an analysis of the interplay between fire incidents and the corresponding detection mechanisms, the consequences of which have been meticulously scrutinized [ 10 ]. Various models were put to the test by researchers to refine the detection of forest fires, where the YOLOvX models notably exhibited a distinctive proficiency, setting them apart from their counterparts within the realm of detection models [ 11 ]. The study contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV

SHAMTA,

Demir

2024

PLoS ONE

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This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following results: 96% accuracy for YOLOv8 classification, 89% accuracy for YOLOv8n object detection, 96% accuracy for CNN-RCNN classification, and 89% accuracy for YOLOv5n object detection.

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Early Wildfire Smoke Detection Using Different YOLO Models

Cited by 33 publications

References 23 publications

G-YOLO: A YOLOv7-based target detection algorithm for lightweight hazardous chemical vehicles

G-YOLO: A YOLOv7-based target detection algorithm for lightweight hazardous chemical vehicles

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches

Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV

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