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学者姓名：李建微
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Abstract ：
The acceleration of global warming and intensifying global climate anomalies have led to a rise in the frequency of wildfires. However, most existing research on wildfire fields focuses primarily on wildfire identification and prediction, with limited attention given to the intelligent interpretation of detailed information, such as fire front within fire region. To address this gap, advance the analysis of fire front in UAV-captured visible images, and facilitate future calculations of fire behavior parameters, a new method is proposed for the intelligent segmentation and fire front interpretation of wildfire regions. This proposed method comprises three key steps: deep learning-based fire segmentation, boundary tracking of wildfire regions, and fire front interpretation. Specifically, the YOLOv7-tiny model is enhanced with a Convolutional Block Attention Module (CBAM), which integrates channel and spatial attention mechanisms to improve the model's focus on wildfire regions and boost the segmentation precision. Experimental results show that the proposed method improved detection and segmentation precision by 3.8 % and 3.6 %, respectively, compared to existing approaches, and achieved an average segmentation frame rate of 64.72 Hz, which is well above the 30 Hz threshold required for real-time fire segmentation. Furthermore, the method's effectiveness in boundary tracking and fire front interpreting was validated using an outdoor grassland fire fusion experiment's real fire image data. Additional tests were conducted in southern New South Wales, Australia, using data that confirmed the robustness of the method in accurately interpreting the fire front. The findings of this research have potential applications in dynamic data-driven forest fire spread modeling and fire digital twinning areas. The code and dataset are publicly available at https://github.com/makemoneyokk/fire-segmentation-interpretation.git.
Keyword ：
Attention mechanism Attention mechanism Convolutional neural network Convolutional neural network Deep learning Deep learning Fire front interpretation Fire front interpretation Unmanned aerial vehicle Unmanned aerial vehicle Wildfire segmentation Wildfire segmentation
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| GB/T 7714 | Li, Jianwei , Wan, Jiali , Sun, Long et al. Intelligent segmentation of wildfire region and interpretation of fire front in visible light images from the viewpoint of an unmanned aerial vehicle (UAV) [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 220 : 473-489 . |
| MLA | Li, Jianwei et al. "Intelligent segmentation of wildfire region and interpretation of fire front in visible light images from the viewpoint of an unmanned aerial vehicle (UAV)" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 220 (2025) : 473-489 . |
| APA | Li, Jianwei , Wan, Jiali , Sun, Long , Hu, Tongxin , Li, Xingdong , Zheng, Huiru . Intelligent segmentation of wildfire region and interpretation of fire front in visible light images from the viewpoint of an unmanned aerial vehicle (UAV) . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 220 , 473-489 . |
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Wildfires, a persistent environmental menace, are a significant source of harmful gases and particulate emissions. This study leverages the fire radiative power (FRP) method to delineate a comprehensive wildfire emission inventory for Southwest China from 2001 to 2020. Daily fire radiative power data derived from 1 km MODIS Thermal Anomalies/Fire products (MOD14/MYD14) were used to calculate the FRE and combusted biomass. Available emission factors were assigned to three biomass burn types: forest, grass, and shrub fires. Over the span of two decades, we have compiled data and estimated the annual emissions of carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), sulfur dioxide (SO2), ammonia (NH3), nitrogen oxides (NOx), total particulate matter (TPM), black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) to be 9809.13, 566.82, 25.79, 5.37, 12.25, 16.67, 133.53, 4.16, 41.81, and 97.23 Gg per year (Gg yr(-1)), respectively. In terms of fire type, forest fires accounted for the largest portion of total CO2 emissions (59.23%), with grass fires and shrub fires coming in second and third, accounting for 20.41% and 20.36%, respectively. Geographically, Yunnan Province were identified as the major contributor in Southwest China, accounting for 69.67% of the total emissions. Temporally, the maximum emission occurred in 2010 (24263.33 Gg), and the minimum emission occurred in 2017 (2917.66 Gg). And the emissions were mainly concentrated in February (23.33%), March (25.52%), and April (22.61%), which accounted for nearly three-fourths of the total emissions. The results of this study are much higher than those obtained by the burned area method, almost three times as high. In contrast, the results of this study are close to the fire emission data from the GFED4s and GFASv1.2 and QFEDv2.5r1 databases.
Keyword ：
Emission inventory Emission inventory Fire radiative power Fire radiative power Forest fire Forest fire Southwest China Southwest China Wildfire Wildfire
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| GB/T 7714 | Ning, Xincen , Li, Jianwei , Zhuang, Pengkun et al. Wildfire combustion emission inventory in Southwest China (2001-2020) based on MODIS fire radiative energy data [J]. | ATMOSPHERIC POLLUTION RESEARCH , 2024 , 15 (11) . |
| MLA | Ning, Xincen et al. "Wildfire combustion emission inventory in Southwest China (2001-2020) based on MODIS fire radiative energy data" . | ATMOSPHERIC POLLUTION RESEARCH 15 . 11 (2024) . |
| APA | Ning, Xincen , Li, Jianwei , Zhuang, Pengkun , Lai, Shifu , Zheng, Xiaogan . Wildfire combustion emission inventory in Southwest China (2001-2020) based on MODIS fire radiative energy data . | ATMOSPHERIC POLLUTION RESEARCH , 2024 , 15 (11) . |
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To recognize and locate fire accurately, we developed a wildfire-locating method based on superpixel features. Previous research detected fire at the pixel level of the image using deep learning, whereas we preprocessed and classified superpixels for fire recognition and locating. Firstly, a simple linear iterative clustering algorithm was created to segment the image into superpixel blocks. Then, color, texture, and shape features were extracted from each superpixel, and a convolutional neural network (CNN) was employed to classify the superpixels into two categories: fire superpixels and background superpixels. Finally, superpixels were refined based on the superpixel adjacency relationship. Experimental results demonstrated that the combined approach of superpixel features and CNNs performed satisfactory segmentation performance with an accuracy of 96.58%, which was effective in wildfire-locating. © 2024 IEEE.
Keyword ：
Clustering algorithms Clustering algorithms Convolution Convolution Convolutional neural networks Convolutional neural networks Deep learning Deep learning Feature extraction Feature extraction Fires Fires Image segmentation Image segmentation Iterative methods Iterative methods Location Location Superpixels Superpixels Textures Textures
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| GB/T 7714 | Tang, Huan , Zheng, Xiaogan , Guo, Wei et al. Fire-locating Based on Superpixel Features by Convolutional Neural Network [C] . 2024 : 200-205 . |
| MLA | Tang, Huan et al. "Fire-locating Based on Superpixel Features by Convolutional Neural Network" . (2024) : 200-205 . |
| APA | Tang, Huan , Zheng, Xiaogan , Guo, Wei , Wan, Jiali , Li, Jianwei . Fire-locating Based on Superpixel Features by Convolutional Neural Network . (2024) : 200-205 . |
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人群分布不均、遮挡和背景干扰等问题使得人群计数成为了一项复杂且具有挑战性的任务.针对这些问题,提出了一种多尺度特征融合的位置关注网络(Position-Aware Network based on Multi-Scale Feature Fusion,MSF-PANet).首先,设计了一种多尺度特征融合模块,以在不同感受野下提取并融合人群密度图的多尺度特征,同时提取出前景信息,来应对人群计数中的遮挡和背景干扰问题;然后,通过位置注意力分配网络提高模型对人群区域的关注度,有效地应对人群分布不均的问题;最后,为了辅助模型训练,减小背景噪声带来的干扰,引入了一种结构交叉损失用于强化模型对人群结构的学习.实验结果表明:MSF-PANet在Shanghai Tech Part A、Shanghai Tech Part B、UCF-QNRF和UCF_CC_50 上平均绝对误差分别为 59.5、7.8、103、182.7,均方误差分别为 96.7、13.6、177、237.7,验证了所提模块在提高人群计数准确率上的有效性.
Keyword ：
人群密度估计 人群密度估计 人群计数 人群计数 多尺度特征 多尺度特征 注意力机制 注意力机制 背景分割 背景分割
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| GB/T 7714 | 谢劭卓 , 李建微 . 基于多尺度特征融合与位置关注网络的人群计数研究 [J]. | 微电子学与计算机 , 2024 , 41 (8) : 22-30 . |
| MLA | 谢劭卓 et al. "基于多尺度特征融合与位置关注网络的人群计数研究" . | 微电子学与计算机 41 . 8 (2024) : 22-30 . |
| APA | 谢劭卓 , 李建微 . 基于多尺度特征融合与位置关注网络的人群计数研究 . | 微电子学与计算机 , 2024 , 41 (8) , 22-30 . |
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Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual-reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies.
Keyword ：
digital twin digital twin fire detection fire detection fire spread model fire spread model visualization visualization wildfires wildfires
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| GB/T 7714 | Huang, Yuting , Li, Jianwei , Zheng, Huiru . Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques [J]. | FIRE-SWITZERLAND , 2024 , 7 (11) . |
| MLA | Huang, Yuting et al. "Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques" . | FIRE-SWITZERLAND 7 . 11 (2024) . |
| APA | Huang, Yuting , Li, Jianwei , Zheng, Huiru . Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques . | FIRE-SWITZERLAND , 2024 , 7 (11) . |
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Background Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly and are more intense than conventional wildfires. Detecting extreme wildfires is challenging due to their visual similarities to traditional fires, and existing models primarily detect the presence or absence of fires without focusing on distinguishing extreme wildfires and providing warnings.Aims To test a system for real time detection of four extreme wildfires.Methods We proposed a novel lightweight model, called LEF-YOLO, based on the YOLOv5 framework. To make the model lightweight, we introduce the bottleneck structure of MobileNetv3 and use depthwise separable convolution instead of conventional convolution. To improve the model's detection accuracy, we apply a multiscale feature fusion strategy and use a Coordinate Attention and Spatial Pyramid Pooling-Fast block to enhance feature extraction.Key results The LEF-YOLO model outperformed the comparison model on the extreme wildfire dataset we constructed, with our model having excellent performance of 2.7 GFLOPs, 61 FPS and 87.9% mAP.Conclusions The detection speed and accuracy of LEF-YOLO can be utilised for the real-time detection of four extreme wildfires in forest fire scenes.Implications The system can facilitate fire control decision-making and foster the intersection between fire science and computer science. We tested a lightweight architecture called LEF-YOLO for detecting four extreme wildfires. We found improved detection accuracy through multi-scale fusion and attention mechanism, and constructed four extreme wildfire datasets and compared these with multiple object detection models and lightweight feature extraction networks. This method is beneficial for the development of extreme wildfire field robots.
Keyword ：
convolutional neural networks convolutional neural networks deep learning deep learning extreme wildfire extreme wildfire fire safety fire safety lightweight lightweight multiscale feature fusion multiscale feature fusion object detection object detection YOLO (LEF-YOLO) YOLO (LEF-YOLO)
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| GB/T 7714 | Li, Jianwei , Tang, Huan , Li, Xingdong et al. LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework [J]. | INTERNATIONAL JOURNAL OF WILDLAND FIRE , 2024 , 33 (1) . |
| MLA | Li, Jianwei et al. "LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework" . | INTERNATIONAL JOURNAL OF WILDLAND FIRE 33 . 1 (2024) . |
| APA | Li, Jianwei , Tang, Huan , Li, Xingdong , Dou, Hongqiang , Li, Ru . LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework . | INTERNATIONAL JOURNAL OF WILDLAND FIRE , 2024 , 33 (1) . |
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Accurate and reliable segmentation of the pancreas and its lesions on computed tomography (CT) images is crucial in medical imaging for preoperative diagnosis, surgical planning, and postoperative monitoring. However, there are limited studies that address simultaneous segmentation of the pancreas and pancreatic tumors. Moreover, existing studies have not fully utilized the feature potential of the original images and have neglected the exploration of semantic information with strong representation. To overcome these limitations, we propose the Strongly Representative Semantic-guided Segmentation Network (SRSNet). Specifically, we employ intermediate semantic information to generate strongly representative high-resolution pre-segmented images, effectively reducing channel redundancy across different resolutions. We utilize various mechanisms to extract distinct representative features, and with the guidance of these features, SRSNet effectively supplements high-resolution detailed information for features of different resolutions, provides auxiliary features for the pixel decision phase of the network, and detects large-scale changes in the pancreas and pancreatic tumors. Additionally, we design a loss function that enhances SRSNet's sensitivity to boundary pixels and attenuates the effect of class imbalance. Our method is evaluated on Task07 Pancreas and NIH Pancreas datasets. In the experiment of combined pancreas and tumor segmentation in the MSD dataset, we achieved Dice, Recall, Precision, and MIoU scores of 78.60%, 79.64%, 81.72%, and 71.47%, respectively. Extensive experiments demonstrate that our algorithm not only outperforms state-of-the-art algorithms for pancreas segmentation but also exhibits excellent performance for pancreas and pancreatic tumor segmentation.
Keyword ：
High resolution High resolution Lightweight Lightweight Pancreas Pancreas Pancreatic cyst Pancreatic cyst Priori probability Priori probability
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| GB/T 7714 | Cao, Luyang , Li, Jianwei . Strongly representative semantic-guided segmentation network for pancreatic and pancreatic tumors [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 87 . |
| MLA | Cao, Luyang et al. "Strongly representative semantic-guided segmentation network for pancreatic and pancreatic tumors" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 87 (2024) . |
| APA | Cao, Luyang , Li, Jianwei . Strongly representative semantic-guided segmentation network for pancreatic and pancreatic tumors . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 87 . |
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Existing neural network segmentation schemes perform well in the task of segmenting images of organs with large areas and clear morphology, such as the liver and lungs. However, it is difficult to segment organs with variable morphology and small target area, such as pancreas and tumors. In order to achieve accurate seg-mentation of pancreas and its cysts, MDAG-Net (Multi-dimensional Attention Gate Network) is proposed in this paper. Combining three attention mechanisms: spatial, channel and multi-dimensional feature map input, MDAG (Multi-dimensional Attention Gate) obtains the global distribution of semantic information in spatial and channel dimensions, filters redundant information in shallow feature maps, realizes feature response, and recalibrates convolution kernel parameters. In addition, the WML(Weighted cross entropy and MIoU loss function) loss can adaptively assign the weight of category loss and count the classification error of global pixels, which can in-crease the error attention of the target area and improving the segmentation accuracy of the network. The al-gorithm is experimented on the Task07_Pancreas dataset, compared with U-Net under the same conditions, the Dice coefficient, Precision, Recall rate and MIoU (Mean Intersection over Union) of MDAG-Net are improved by 5.3%, 1.5%, 12.7% and 7.6% respectively. The results show that MDAG-Net can accurately segment the region of pancreas and its cyst in CT(Computed Tomography) images, which proves that MDAG has better segmentation efficiency for such small target regions.
Keyword ：
Attention mechanism Attention mechanism Cross entropy loss Cross entropy loss Multi-object segmentation Multi-object segmentation Pancreas Pancreas Pancreatic tumor Pancreatic tumor Small target detection Small target detection
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| GB/T 7714 | Cao, Luyang , Li, Jianwei , Chen, Shu . Multi-target segmentation of pancreas and pancreatic tumor based on fusion of attention mechanism [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 79 . |
| MLA | Cao, Luyang et al. "Multi-target segmentation of pancreas and pancreatic tumor based on fusion of attention mechanism" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 79 (2023) . |
| APA | Cao, Luyang , Li, Jianwei , Chen, Shu . Multi-target segmentation of pancreas and pancreatic tumor based on fusion of attention mechanism . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 79 . |
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本实用新型提出沉浸式火灾演练设备智能运输车，包括智能小车、采集器、3d雾化火焰装置，所述3d雾化火焰装置固定于智能小车上部，3d雾化火焰装置通过设于火灾演练现场的采集器采集演练信息数据，采集器根据演练信息数据生成火焰状态数据并传输给3d雾化火焰装置，用于调整雾化火焰状态以实现人机交互；本实用新型能解决现有模拟火灾演练形式难以平衡成本、安全性与沉浸感的问题。
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| GB/T 7714 | 陈洪珏 , 李建微 , 邱晓苏 et al. 沉浸式火灾演练设备智能运输车 : CN202221270924.X[P]. | 2022-05-25 00:00:00 . |
| MLA | 陈洪珏 et al. "沉浸式火灾演练设备智能运输车" : CN202221270924.X. | 2022-05-25 00:00:00 . |
| APA | 陈洪珏 , 李建微 , 邱晓苏 , 吴钟华 , 郑含静 . 沉浸式火灾演练设备智能运输车 : CN202221270924.X. | 2022-05-25 00:00:00 . |
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本实用新型涉及一种用于森林防火的蛇形仿生爬树修枝机器人，包括依次连接的若干个组块，其中首端、末端的组块上分别转动铰接有摆转架和转动铰接在摆转架上的驱动轮，在首端与末端组块之间的各组块的底面设有滚轮，所述首端和末端的组块上具有自底面往顶面延伸的缺口，所述摆转架和驱动轮设置在缺口位置内，并使驱动轮的下端与各滚轮的下端处于同一水平面，其中一组块上连接有用于锯切树枝的电锯。本实用新型一种用于森林防火的蛇形仿生爬树修枝机器人的优点，由于摆转架和驱动轮设置在缺口位置内，使驱动轮的下端与各滚轮的下端处于同一水平面，从而使该机器人在行进过程中不会由于不平齐的行进轮造成过大的摩擦力，影响行进的顺畅和速度。
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| GB/T 7714 | 郑孝干 , 李建微 , 廖成师 et al. 用于森林防火的蛇形仿生爬树修枝机器人 : CN202320926403.3[P]. | 2023-04-23 00:00:00 . |
| MLA | 郑孝干 et al. "用于森林防火的蛇形仿生爬树修枝机器人" : CN202320926403.3. | 2023-04-23 00:00:00 . |
| APA | 郑孝干 , 李建微 , 廖成师 , 林信恩 , 冯振波 , 赵万涛 et al. 用于森林防火的蛇形仿生爬树修枝机器人 : CN202320926403.3. | 2023-04-23 00:00:00 . |
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