MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection

Abstract

Unmanned aerial vehicles (UAVs) are widely used for small target detection of forest fires due to its low-risk rate, low cost and high ground coverage. However, the detection accuracy of small target forest fires is still not ideal due to its irregular shape, different scale and how easy it can be blocked by obstacles. This paper proposes a multi-scale feature extraction model (MS-FRCNN) for small target forest fire detection by improving the classic Faster RCNN target detection model. In the MS-FRCNN model, ResNet50 is used to replace VGG-16 as the backbone network of Faster RCNN to alleviate the gradient explosion or gradient dispersion phenomenon of VGG-16 when extracting the features. Then, the feature map output by ResNet50 is input into the Feature Pyramid Network (FPN). The advantage of multi-scale feature extraction for FPN will help to improve the ability of the MS-FRCNN to obtain detailed feature information. At the same time, the MS-FRCNN uses a new attention module PAM in the Regional Proposal Network (RPN), which can help reduce the influence of complex backgrounds in the images through the parallel operation of channel attention and space attention, so that the RPN can pay more attention to the semantic and location information of small target forest fires. In addition, the MS-FRCNN model uses a soft-NMS algorithm instead of an NMS algorithm to reduce the error deletion of the detected frames. The experimental results show that, compared to the baseline model, the proposed MS-FRCNN in this paper achieved a better detection performance of small target forest fires, and its detection accuracy was 5.7% higher than that of the baseline models. It shows that the strategy of multi-scale image feature extraction and the parallel attention mechanism to suppress the interference information adopted in the MS-FRCNN model can really improve the performance of small target forest fire detection.

Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model

“…For their study, Zhang et al [17] created the multi-scale feature extraction model (MS-FRCNN) for the detection of small target forest fires. This model enhances the conventional Faster RCNN detection technique.…”

FuF-Det: An Early Forest Fire Detection Method under Fog

“…Various target detection algorithms have gradually matured and have been widely applied in forest fire detection tasks in recent years. For two-stage target detection algorithms, Zhang et al [17] proposed the MS-FRCNN algorithm to address the early-stage detection of small fire points. Based on Faster R-CNN, it incorporates the FPN structure to fuse multi-scale information and introduces an attention mechanism into the region proposal network.…”

“…Prescribed fire refers to the deliberate ignition of potential fuels in the forest under the conditions of maximum temperature, relative humidity and wind speed at the threshold required for fire to spread [5] in order to reduce the fuel density in the forest and prevent the occurrence of destructive forest fires [6]. For forest fire monitoring, common methods can be categorized into four types: manual patrols, sensor-based monitoring [7][8][9], fire satellite monitoring [10][11][12][13][14], and UAV monitoring [15][16][17][18]. Manual patrols are inefficient, have limited coverage, and expose humans to potential risks in hazardous environments [19].…”

Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV

“…Forest fires, increasingly common today, require rigorous scientific exploration of early detection methods. This is crucial to proactively control their spread and mitigate environmental harm [ 1 , 2 ]. The advent of the Industrial Revolution marked a pivotal moment in Earth’s history, initiating a series of events that significantly altered global fire patterns.…”