Forest Fire Smoke Recognition Based on Anchor Box Adaptive Generation Method

Enting Zhao,Ye Tian,Junguo Zhang,Yang Liu

doi:10.3390/electronics10050566

Abstract

There are major problems in the field of image-based forest fire smoke detection, including the low recognition rate caused by the changeable and complex state of smoke in the forest environment and the high false alarm rate caused by various interferential objects in the recognition process. Here, a forest fire smoke identification method based on the integration of environmental information is proposed. The model uses (1) the Faster R-CNN as the basic framework, (2) a component perception module to generate a receptive field of integrated environmental information through separable convolution to improve recognition accuracy, and (3) a multi-level Region of Interest (ROI)pooling structure to reduce the deviation caused by rounding in the ROI pooling process. The results showed that the model achieved a recognition accuracy rate of 96.72%, an Intersection Over Union (IOU) of 78.96%, and an average recognition speed for each picture of 1.5 ms; the false alarm rate was 2.35% and the false-negative rate was 3.28%. Compared with other models, the proposed model can effectively enhance the recognition accuracy and recognition speed of forest fire smoke, which provides a technical basis for the real-time and accurate detection of forest fires.

Highlights

Forest fire is one of the major environmental disasters threatening the safety of forestry workers and the ecological balance
From January to August 2019, a total of 1563 forest fires occurred in China, which affected an area of 8518 hectares and resulted in substantial ecological damage and economic losses
CToomvperairfaytitvheeEsxuppeerriimoernittyooffDthifefefroernetsRt fiecroegsnmitoiokneMreocdoeglnsition model proposed in this articleTaonvdecroifmypthareesiut wpeitrhiothrietyreocfogthneitifoonreesfftefcitrseosfmtwookecormecmogonnliytiuosnedmtoadrgeeltpdreotpecotisoend in this article and compare it with the recognition effects of two commonly used target detection models (Yolo-V3 and SSD) and the latest target detection models (FCOS and EfficientDet) with Faster R-CNN, 1110 images of fire smoke at different viewing distances in the dataset PART 3 were selected

Summary

Introduction

Forest fire is one of the major environmental disasters threatening the safety of forestry workers and the ecological balance. From January to August 2019, a total of 1563 forest fires occurred in China, which affected an area of 8518 hectares and resulted in substantial ecological damage and economic losses. The effective prevention and control of forest fires have become a major focus of scientific research. The early detection and warning of fires is critically important for fire prevention. When a forest fire occurs, it is often accompanied by smoke. Given that smoke can be more detected because of its wider distribution compared to flames, it is a robust indicator for the presence of forest fires in the early stages. Forest fires can be detected so that fire-fighting measures can be quickly implemented, and the harm caused by forest fires can be reduced

Methods

Results

Discussion

Conclusion