CNN-based fire detection method on autonomous ships using composite channels composed of RGB and IR data

Kim, DongHyun; Ruy, Won-Sun

doi:10.1016/j.ijnaoe.2022.100489

Cited by 19 publications

(6 citation statements)

References 36 publications

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“…Few studies have focused on ship fires. Kim et al [44] proposed an innovative method that integrates composite channel data, namely, RGB and IR channels, to enhance the effectiveness of fire detection in image-based systems by using convolutional neural networks (CNNs); Park et al [19] used YOLO to detect fires in the engine room of a ship. Xu et al [20] proposed an evaluation model based on a CNN that could determine the hazard levels of different cabins in a realtime fire; Wu et al [21] proposed a modified YOLOv4-tiny algorithm for detecting ship fires; Avazov et al [22] used YOLOv7 with an improved E-ELAN (extended efficient layer aggregation network) for fire detection and monitoring; and Zhu et al [45] improved the YOLOv7-tiny model to enhance fire detection performance in a ship engine room.…”

Section: Deep Learningmentioning

confidence: 99%

Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection

Zhang,

Tan,

Tiong

2024

Sensors

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Ship fire may result in significant damage to its structure and large economic loss. Hence, the prompt identification of fires is essential in order to provide prompt reactions and effective mitigation strategies. However, conventional detection systems exhibit limited efficacy and accuracy in detecting targets, which has been mostly attributed to limitations imposed by distance constraints and the motion of ships. Although the development of deep learning algorithms provides a potential solution, the computational complexity of ship fire detection algorithm pose significant challenges. To solve this, this paper proposes a lightweight ship fire detection algorithm based on YOLOv8n. Initially, a dataset, including more than 4000 unduplicated images and their labels, is established before training. In order to ensure the performance of algorithms, both fire inside ship rooms and also fire on board are considered. Then after tests, YOLOv8n is selected as the model with the best performance and fastest speed from among several advanced object detection algorithms. GhostnetV2-C2F is then inserted in the backbone of the algorithm for long-range attention with inexpensive operation. In addition, spatial and channel reconstruction convolution (SCConv) is used to reduce redundant features with significantly lower complexity and computational costs for real-time ship fire detection. For the neck part, omni-dimensional dynamic convolution is used for the multi-dimensional attention mechanism, which also lowers the parameters. After these improvements, a lighter and more accurate YOLOv8n algorithm, called Ship-Fire Net, was proposed. The proposed method exceeds 0.93, both in precision and recall for fire and smoke detection in ships. In addition, the mAP@0.5 reaches about 0.9. Despite the improvement in accuracy, Ship-Fire Net also has fewer parameters and lower FLOPs compared to the original, which accelerates its detection speed. The FPS of Ship-Fire Net also reaches 286, which is helpful for real-time ship fire monitoring.

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Section: Deep Learningmentioning

confidence: 99%

Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection

Zhang,

Tan,

Tiong

2024

Sensors

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“…Kim and Ruy [92] used three-channel color images and one-channel IR data. An IR image is a 2D matrix with a single channel (gray scale) consisting of temperature values.…”

Section: Foggia Dataset [45]mentioning

confidence: 99%

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Diaconu

2023

Fire

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Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed.

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“…If any of these items catch fire, the results would be catastrophic. Additionally, a fire accident on a ship is highly likely to be fatal to human life because it is difficult to receive fire suppression support from the outside due to the nature of the closed and isolated space of the sea, and it is necessary to extinguish the fire with a limited amount of personnel and equipment [ 2 ].…”

Section: Introductionmentioning

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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CNN-based fire detection method on autonomous ships using composite channels composed of RGB and IR data

Cited by 19 publications

References 36 publications

Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection

Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

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

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