CFS-YOLO: An Early Fire Detection Method via Coarse and Fine Grain Search and Focus Modulation

FANG Xianjin; JIANG Xuefeng; XU Liuquan; FANG Zhongyi

doi:10.11999/JEIT240928

Volume 47 Issue 6

Jun. 2025

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FANG Xianjin, JIANG Xuefeng, XU Liuquan, FANG Zhongyi. CFS-YOLO: An Early Fire Detection Method via Coarse and Fine Grain Search and Focus Modulation[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1976-1991. doi: 10.11999/JEIT240928

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FANG Xianjin, JIANG Xuefeng, XU Liuquan, FANG Zhongyi. CFS-YOLO: An Early Fire Detection Method via Coarse and Fine Grain Search and Focus Modulation[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1976-1991. doi: 10.11999/JEIT240928

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CFS-YOLO: An Early Fire Detection Method via Coarse and Fine Grain Search and Focus Modulation

doi: 10.11999/JEIT240928 cstr: 32379.14.JEIT240928

1.
School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
2.
School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
3.
Anhui Rongyue Information Technology Company Limited, Hefei 231283, China

Funds: The Collaborative Innovation Project of Anhui Universities and the Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center (GXXT-2021-006)

Received Date: 2024-10-24
Rev Recd Date: 2025-02-26

Available Online: 2025-03-05

Publish Date: 2025-06-30

Abstract

Abstract

Objective Fire is a frequent disaster, and detecting early fire phenomena effectively can significantly reduce casualties. Traditional fire detection methods, which rely on sensor devices, struggle to accurately detect fires in open spaces. With the development of deep learning, fire detection can be automated through image capture devices like cameras, improving detection accuracy. However, early-stage fires are small and often obscured by occlusion or fire-like objects. Fire detection models, such as Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLO), often fail to meet real-time detection requirements due to their large number of parameters, which slow down inference. Additionally, existing models face challenges in preserving fire edges and color features, leading to reduced detection accuracy. To address these issues, this paper proposes CFS-YOLO, an early-stage fire recognition model that incorporates coarse- and fine-grained search and focus modulation, enhancing both the speed and accuracy of fire detection. Methods To enhance the detection efficiency of the model, a coarse- and fine-grained search strategy is introduced to optimize the lightweight structure of the Unit Inference Block (UIB) module, which consists of four possible instantiations (Fig. 2). A coarse-grained search quickly evaluates different network architectures by adapting the network topology, adding optional convolutional modules to the UIB, and modifying the arrangement and combination of the modules. Dimensionality tuning is performed during the search process to select feature map dimensions and convolutional kernel sizes, generating candidate architectures by expanding or compressing the network width. During the filtering process, candidate architectures are evaluated based on multiple performance metrics. A multi-objective optimization approach is used to find the Pareto-optimal solution, retaining candidate architectures that balance accuracy and efficiency. Weight sharing is employed to improve parameter reuse. The fine-grained search refines the candidate architectures from the coarse-grained search, dynamically adjusting hyperparameters such as learning rate, batch size, regularization coefficient, and optimization algorithm according to the model's performance during training. It analyzes and adjusts each module layer by layer to accelerate convergence and better adapt to data complexity. To address the challenges posed by complex scenes and interfering objects, a focus-modulated attention mechanism is introduced, as shown in (Fig. 3). The input fire images are processed through a lightweight linear layer, followed by selective aggregation of contextual information to the modulators of each query token through a hierarchical contextualization module and gating mechanism. These aggregated modulators are injected into each query token via affine changes to generate outputs. This approach helps tackle the challenges of detecting small targets or objects in complex backgrounds, effectively capturing long-range dependencies and contextual information in the image. Finally, to account for the effects of anchor shape and angle, the model introduces a ShapeIoU loss function (Fig. 4). This function considers the influence of distance, shape, and angle between the true and prior frames on the predictor’s frame regression, enabling accurate measurement of the similarity between the true and predicted frames. Results and Discussions (Table 1) presents the results of the ablation experiments. The results show that CFS-YOLO achieved optimal performance. Compared to the baseline model, CFS-YOLO improves precision, recall, and F1 score by 13.33%, 4.96%, and 9.36%, respectively, and increases fps by 22. The model also shows significant improvements in APflame, APsmoke, and mAP, with increases of 11.1%, 16.2%, and 13.65%, respectively, validating the model’s effectiveness. (Fig. 6) illustrates the detection heat map for the ablation model, demonstrating that the combination of the focus-modulated attention mechanism and the ShapeIoU loss function effectively captures key features, confirming their synergistic effect. (Fig. 7) shows the loss curve plots for IoU and ShapeIoU. At the 80th training cycle, the loss of the baseline model stabilizes and converges to 0.5. In contrast, the bounding box loss and DFL loss with the ShapeIoU loss function converge to 0.3 by the 40th cycle, while the classification loss reaches 0.15 by the 80th epoch, highlighting the effectiveness of the ShapeIoU loss function. (Table 2) compares the performance with several state-of-the-art target detection models, while (Table 3) presents a comparison of different flame detection algorithms.The results show that CFS-YOLO leads in performance and demonstrates higher computational efficiency, indicating its potential application value in the flame detection field. (Fig. 10) and (Fig. 11) provide visualizations of the CFS-YOLO detection results, showing its excellent performance in capturing fire information despite background interference and small fire targets. Conclusions CFS-YOLO demonstrates outstanding performance in early fire detection, achieving detection speeds of up to 75 frame. It provides high inference speeds, meeting the requirements for real-time detection. Compared to state-of-the-art object detection models, CFS-YOLO outperforms in both detection accuracy and speed.
- Early fire,
- Fire detection,
- Coarse and fine-grained search,
- Focus-modulated attention mechanisms,
- ShapeIoU

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