An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

Zhang, Long; Li, Jiaming; Zhang, Fuquan

doi:10.3390/fire6080291

Cited by 10 publications

(4 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forest fires are dynamic objects with shapes that constantly change and textures that are difficult to accurately depict [41]. Traditional methods have advantages in forest fire recognition in terms of speed and accuracy.…”

Section: Discussionmentioning

confidence: 99%

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Mu,

Ou,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies.

show abstract

Section: Discussionmentioning

confidence: 99%

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Mu,

Ou,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…In recent years, the frequency and severity of forest fires have increased significantly over much of the world [3]. Largescale forest fires can result in considerable environmental damage [4][5][6], disrupting the composition and structure of ecosystems significantly [7,8]. Forest fire risk in China shows an increasing trend, with more areas under the high-risk zone [9],highlighting the urgency of responding to this escalating threat.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Xu,

Chen,

Xie

et al. 2024

Fire

View full text Add to dashboard Cite

Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires.

show abstract

“…Sun, X et al implemented the real-time inspection of power systems using the YOLOv4 algorithm, but the proposed method is susceptible to the interference of background information, and it is difficult to realize the identification of multiple overlapping targets [ 23 ]. Zhang, L. et al developed the YOLOv5 algorithm to solve the problem of the long-range detection of small target sizes in forest fires, and the accuracy rate was significantly improved, but the method has more parameters, and the detection speed is slower [ 24 ]. Pullakandam, M. et al developed the YOLOv8 algorithm to realize the abnormal behavior detection of surveillance cameras; compared with YOLOv5, the mAP value was increased by 1%, and the detection speed was also greatly improved [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8

Gao,

Liu,

Chui

et al. 2024

Sensors

View full text Add to dashboard Cite

In this work, an object detection method using variable convolution-improved YOLOv8 is proposed to solve the problem of low accuracy and low efficiency in detecting spanning and irregularly shaped samples. Aiming at the problems of the irregular shape of a target, the low resolution of labeling frames, dense distribution, and the ease of overlap, a deformable convolution module is added to the original backbone network. This allows the model to deal flexibly with the problem of the insufficient perceptual field of the target corresponding to the detection point, and the situations of leakage and misdetection can be effectively improved. In order to solve the issue that small target detection is susceptible to image background and noise interference, the Sim-AM (simple parameter-free attention mechanism) module is added to the backbone network of YOLOv8, which enhances the attention to the underlying features and, thus, improves the detection accuracy of the model. More importantly, the Sim-AM module does not need to add parameters to the original network, which reduces the computation of the model. To address the problem of complex model structures that can lead to slower detection, the spatial pyramid pooling of the backbone network is replaced with focal modulation networks, which greatly simplifies the computation process. The experimental validation was carried out on the scrap steel dataset containing a large number of targets of multiple shapes and sizes. The results showed that the improved YOLOv8 network model improves the AP (average precision) by 2.1%, the mAP (mean average precision value) by 0.8%, and reduces the FPS (frames per second) by 5.4, which meets the performance requirements of real-time industrial inspection.

show abstract

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

Cited by 10 publications

References 60 publications

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8

Contact Info

Product

Resources

About