Early smoke and flame detection based on transformer

Wang, Xinzhi; Li, Mengyue; Gao, Mingke; Liu, Quanyi; Li, Zhennan; Kou, Luyao

doi:10.1016/j.jnlssr.2023.06.002

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…Considering that there are many smoke interferences in the process of forest fire recognition, which affect the presentation of forest fire images and the recognition and extraction of forest fire image features, and influence the accuracy of recognition, further analysis and experiments on how to exclude the influence of smoke features on forest fire images will be carried out in the following to improve the generalizability of the model [30,31].…”

Section: Discussionmentioning

confidence: 99%

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

Qian,

Bai,

Jiao

et al. 2023

Forests

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Forest fires are major forestry disasters that cause loss of forest resources, forest ecosystem safety, and personal injury. It is often difficult for current forest fire detection models to achieve high detection accuracy on both large and small targets at the same time. In addition, most of the existing forest fire detection models are single detection models, and using only a single model for fire detection in a complex forest environment has a high misclassification rate, and the accuracy rate needs to be improved. Aiming at the above problems, this paper designs two forest fire detection models (named WSB and WSS) and proposes an integrated learning-based forest fire detection model (named WSB_WSS), which also obtains high accuracy in the detection of forest fires with large and small targets. In order to help the model predict the location and size of forest fire targets more accurately, a new edge loss function, Wise-Faster Intersection over Union (WFIoU), is designed in this paper, which effectively improves the performance of the forest fire detection algorithm. The WSB model introduces the Simple-Attention-Module (SimAM) attention mechanism to make the image feature extraction more accurate and introduces the bi-directional connectivity and cross-layer feature fusion to enhance the information mobility and feature expression ability of the feature pyramid network. The WSS model introduces the Squeeze-and-Excitation Networks (SE) attention mechanism so that the model can pay more attention to the most informative forest fire features and suppress unimportant features, and proposes Spatial Pyramid Pooling-Fast Cross Stage Partial Networks (SPPFCSPC) to enable the network to extract features better and speed up the operation of the model. The experimental findings demonstrate that the WSB model outperforms other approaches in the context of identifying forest fires characterized by small-scale targets, achieving a commendable accuracy rate of 82.4%, while the WSS model obtains a higher accuracy of 92.8% in the identification of large target forest fires. Therefore, in this paper, a more efficient forest fire detection model, WSB_WSS, is proposed by integrating the two models through the method of Weighted Boxes Fusion (WBF), and the accuracy of detecting forest fires characterized by small-scale targets attains 83.3%, while for forest fires with larger dimensions, the accuracy reaches an impressive 93.5%. This outcome effectively leverages the strengths inherent in both models, consequently achieving the dual objective of high-precision detection for both small and large target forest fires concurrently.

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Section: Discussionmentioning

confidence: 99%

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

Qian,

Bai,

Jiao

et al. 2023

Forests

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“…Small-scale local features may not adequately reflect the characteristics of fire regions and can struggle to suppress background noise. Consequently, there is a need to design network modules with the capability to extract global features, aiming to address the limitations of traditional convolutional neural networks 29 . This study draws inspiration from the recently introduced CoT module, a context-aware attention mechanism 30 .…”

Section: Methodsmentioning

confidence: 99%

A lightweight fire detection algorithm for small targets based on YOLOv5s

Lv,

Zhou,

Chen

et al. 2024

Sci Rep

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In response to the current challenges fire detection algorithms encounter, including low detection accuracy and limited recognition rates for small fire targets in complex environments, we present a lightweight fire detection algorithm based on an improved YOLOv5s. The introduction of the CoT (Contextual Transformer) structure into the backbone neural network, along with the creation of the novel CSP1_CoT (Cross stage partial 1_contextual transformer) module, has effectively reduced the model’s parameter count while simultaneously enhancing the feature extraction and fusion capabilities of the backbone network; The network’s Neck architecture has been extended by introducing a dedicated detection layer tailored for small targets and incorporating the SE (Squeeze-and-Excitation) attention mechanism. This augmentation, while minimizing parameter proliferation, has significantly bolstered the interaction of multi-feature information, resulting in an enhanced small target detection capability; The substitution of the original loss function with the Focal-EIoU (Focal-Efficient IoU) loss function has yielded a further improvement in the model’s convergence speed and precision; The experimental results indicate that the modified model achieves an mAP@.5 of 96% and an accuracy of 94.8%, marking improvements of 8.8% and 8.9%, respectively, over the original model. Furthermore, the model’s parameter count has been reduced by 1.1%, resulting in a compact model size of only 14.6MB. Additionally, the detection speed has reached 85 FPS (Frames Per Second), thus satisfying real-time detection requirements. This enhancement in precision and accuracy, while simultaneously meeting real-time and lightweight constraints, effectively caters to the demands of fire detection.

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“…Wang et al [29] used an object detector based on the vision transformer architecture to detect smaller flame and smoke areas. They used a private dataset and a public dataset, called the fire smoke dataset.…”

Section: Related Workmentioning

confidence: 99%

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

Gonçalves,

Ghali,

Akhloufi

2024

Fire

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Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make their detection a challenging task. In this paper, recent YOLO-based algorithms are adopted and implemented for detecting and localizing smoke and wildfires within ground and aerial images. Notably, the YOLOv7x model achieved the best performance with an mAP (mean Average Precision) score of 80.40% and fast detection speed, outperforming the baseline models in detecting both smoke and wildfires. YOLOv8s obtained a high mAP of 98.10% in identifying and localizing only wildfire smoke. These models demonstrated their significant potential in handling challenging scenarios, including detecting small fire and smoke areas; varying fire and smoke features such as shape, size, and colors; the complexity of background, which can include diverse terrain, weather conditions, and vegetation; and addressing visual similarities among smoke, fog, and clouds and the the visual resemblances among fire, lighting, and sun glare.

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Early smoke and flame detection based on transformer

Cited by 12 publications

References 24 publications

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

A lightweight fire detection algorithm for small targets based on YOLOv5s

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

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