A Semi-Supervised Method for Real-Time Forest Fire Detection Algorithm Based on Adaptively Spatial Feature Fusion

Abstract

Forest fires occur frequently around the world, causing serious economic losses and human casualties. Deep learning techniques based on convolutional neural networks (CNN) are widely used in the intelligent detection of forest fires. However, CNN-based forest fire target detection models lack global modeling capabilities and cannot fully extract global and contextual information about forest fire targets. CNNs also pay insufficient attention to forest fires and are vulnerable to the interference of invalid features similar to forest fires, resulting in low accuracy of fire detection. In addition, CNN-based forest fire target detection models require a large number of labeled datasets. Manual annotation is often used to annotate the huge amount of forest fire datasets; however, this takes a lot of time. To address these problems, this paper proposes a forest fire detection model, TCA-YOLO, with YOLOv5 as the basic framework. Firstly, we combine the Transformer encoder with its powerful global modeling capability and self-attention mechanism with CNN as a feature extraction network to enhance the extraction of global information on forest fire targets. Secondly, in order to enhance the model’s focus on forest fire targets, we integrate the Coordinate Attention (CA) mechanism. CA not only acquires inter-channel information but also considers direction-related location information, which helps the model to better locate and identify forest fire targets. Integrated adaptively spatial feature fusion (ASFF) technology allows the model to automatically filter out useless information from other layers and efficiently fuse features to suppress the interference of complex backgrounds in the forest area for detection. Finally, semi-supervised learning is used to save a large amount of manual labeling effort. The experimental results show that the average accuracy of TCA-YOLO improves by 5.3 compared with the unimproved YOLOv5. TCA-YOLO also outperformed in detecting forest fire targets in different scenarios. The ability of TCA-YOLO to extract global information on forest fire targets was much improved. Additionally, it could locate forest fire targets more accurately. TCA-YOLO misses fewer forest fire targets and is less likely to be interfered with by forest fire-like targets. TCA-YOLO is also more focused on forest fire targets and better at small-target forest fire detection. FPS reaches 53.7, which means that the detection speed meets the requirements of real-time forest fire detection.

Predicting wildfires in Algerian forests using machine learning models

“… Statistical models : These models use historical data and statistical techniques to establish empirical relationships between fire occurrence and explanatory variables such as weather, vegetation, human activities, etc. Statistical models can provide simple and fast predictions based on available data, but they may not capture the nonlinear and spatiotemporal patterns of fire occurrence, and they may not generalize well to new situations or regions [ 2 ]. …”

A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

“…Lin et al 31 . used BiFPN to replace the multi‐scale feature fusion network of YOLOv5, so that it could better integrate multi‐scale features of tea diseases and enhance the expression ability of the subtle features of tea diseases.…”

“…where W i and W j are learnable weights that are not < 0; ε = 0.0001 makes the denominator always > 0, ensuring the stability of the value. Lin et al 31 used BiFPN to replace the multi-scale feature fusion network of YOLOv5, so that it could better integrate multi-scale features of tea diseases and enhance the expression ability of the subtle features of tea diseases. In the present study, we use the BiFPN structure to replace the PANet structure of the third Concat layer in the Neck of the network and add the output features from layer six to the input channels of this Concat layer to improve the fusion ability of the critical features of various diseases such as stripes of the diseased spots and leaf morphology, such that the model has a more balanced identification effect on various diseases.…”

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

“…YOLOv5 is an object-detection architecture known for its high performance and efficiency, demonstrating rapid, precise, and adaptable features [44]. It has broad applications in various computer vision tasks and provides powerful tools and technical support for real-time object detection.…”