Optimizing Deep Learning Accuracy: Visibility Filtering Approach for Early Wildfire Detection in Forest Sensor Images

Due to the destruction caused by wildfires underscores the critical need for proactive wildfire management strategies. Particularly, early detection of wildfires can mitigate their impact on the environment and economic loss and save human lives. This research study builds upon our prior work on refining and improving identifying early-stage wildfires. Previous research focused on using a deep-learning model for detecting early wildfires. This study aims to classify the visibility levels of forest sensory images to improve the deep learning model. Due to the constraints of existing public wildfire detection datasets, we have created a specialized forest sensory images dataset tailored specifically for detecting wildfires in their initial stages. We have used an Open-source computer vision library (OpenCV) for image processing integrated with TensorFlow and Keras to create a hybrid deep learning model. The results showed a significant improvement in the accuracy from 84% to 87% in detecting early spots of fire in images captured by surveillance cameras or/satellites. The Convolutional Neural Network model has been evaluated by crucial classification metrics, including accuracy, precision, recall, and f1 values. The study aids in progressing the efficacy of early wildfire detection, furnishing valuable perspectives for urban centers and nations contending with the dangers presented by these catastrophic natural events.