MMFNet: Forest Fire Smoke Detection Using Multiscale Convergence Coordinated Pyramid Network With Mixed Attention and Fast-Robust NMS

Zhang, Liangji; Lu, Chao; Xu, Haiwen; Chen, Aibin; Li, Liujun; Zhou, Guofu

doi:10.1109/jiot.2023.3277511

Cited by 17 publications

(9 citation statements)

References 41 publications

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“…By effectively preventing the misidentification of fire spots and smoke, FSNet outperforms traditional CNN and transformer methods. While recent advancements, such as MMFNet [32] and YOLOV8, introduce innovative techniques, they still rely on generating anchors or proposals for detection, which may lead to issues related to low anchor accuracy and occlusion in forestfire images. In contrast, FSNet's innovative approach eliminates the need for anchors or proposals, providing a more efficient and accurate detection mechanism.…”

Section: Discussionmentioning

confidence: 99%

“…An approach called SAP is introduced for weakly supervised forest-fire segmentation in UAV imagery, which enhances foreground awareness for distinguishing object categories in images [1]. MMFNet [32] presents a mixed-attention multiscale convergence coordinated pyramid network and a fast robust NMS for rapid forest-fire-smoke detection. The latest YOLOV8 introduces an anchor-free method as an alternative to traditional anchor methods, avoiding issues related to low anchor accuracy.…”

Section: Forest Fire and Smoke Detectionmentioning

confidence: 99%

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FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

Wu,

Qian,

et al. 2024

Forests

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Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as Lforest, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model.

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Section: Discussionmentioning

confidence: 99%

Section: Forest Fire and Smoke Detectionmentioning

confidence: 99%

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

Wu,

Qian,

et al. 2024

Forests

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“…The false alarm rate is increased by 0.9%, mainly due to the proposed algorithm improving the network's ability to extract features. The GEM attention module presented in this paper performs significantly better than CBAM [34], although the processing speed is relatively slow. Still, it is also almost four times the real-time frame rate, which can provide good real-time processing.…”

Section: Comparisons With Target Detection-based Improved Smoke Detec...mentioning

confidence: 92%

An Improved Forest Smoke Detection Model Based on YOLOv8

Wang,

Piao,

Wang

et al. 2024

Forests

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This study centers on leveraging smoke detection for preemptive forest smoke detection. Owing to the inherent ambiguity and uncertainty in smoke characteristics, existing smoke detection algorithms suffer from reduced detection accuracy, elevated false alarm rates, and occurrences of omissions. To resolve these issues, this paper employs an efficient YOLOv8 network and integrates three novel detection modules for enhancement. These modules comprise the edge feature enhancement module, designed to identify smoke ambiguity features, alongside the multi-feature extraction module and the global feature enhancement module, targeting the detection of smoke uncertainty features. These modifications improve the accuracy of smoke area identification while notably lowering the rate of false alarms and omission phenomenon occurrences. Meanwhile, a large forest smoke dataset is created in this paper, which includes not only smoke images with normal forest backgrounds but also a considerable quantity of smoke images with complex backgrounds to enhance the algorithm’s robustness. The proposed algorithm in this paper achieves an AP of 79.1%, 79.2%, and 93.8% for the self-made dataset, XJTU-RS, and USTC-RF, respectively. These results surpass those obtained by the current state-of-the-art target detection-based and neural network-based improved smoke detection algorithms.

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“…Modified ResNet and GoogleNet are employed for classification [45]. In the recent years, Customized CNN are employed for plant's environmental concerns by incorporating attention networks and cross-layer extraction structures [40][41][42][43]46].…”

Section: Related Workmentioning

confidence: 99%

GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

Karthik,

Menaka,

Ompirakash

et al. 2024

IEEE Access

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Grapes are a widely cultivated crop in the horticultural industry, renowned for their unique flavor and nutritional benefits. However, this crop is highly susceptible to various diseases that can cause significant reductions in yield and quality, resulting in considerable financial losses. Therefore, it is imperative to identify these diseases to effectively manage their spread. Traditionally, the identification of grape leaf diseases has relied on scientific expertise and observational skills. However, with the advent of deep learning methods, it is now feasible to recognize disease patterns from images of infected leaves. In this research, we propose a novel dual-track feature fusion network titled 'GrapeLeafNet' for detecting grape leaf disease. It employs a dual-track feature fusion approach, combining Inception-ResNet blocks with CBAM for local feature extraction and Shuffle-Transformer for global feature extraction. The first track uses Inception-ResNet blocks to represent features at multiple scales and map significant features, and CBAM captures significant spatial and channel dependencies. The second track employs Shuffle-Transformer to extract long-term dependencies and complex global features in images. The extracted features are then fused using Coordinate attention, enabling the network to capture both local and global contextual information. Experimental results on the Grape leaf disease dataset from Plant Village demonstrate the effectiveness of the proposed network, achieving an accuracy of 99.56%. INDEX TERMSGrape leaf disease, deep learning, transformer, CNN, attention I. INTRODUCTION

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MMFNet: Forest Fire Smoke Detection Using Multiscale Convergence Coordinated Pyramid Network With Mixed Attention and Fast-Robust NMS

Cited by 17 publications

References 41 publications

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

An Improved Forest Smoke Detection Model Based on YOLOv8

GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification

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