Drone-based Forest fire detection: a comparison of convolutional neural networks with an emphasis on MobileNetV2 enhancements

Drone image recognition plays a significant role in identifying forest fires. This paper applies convolutional neural networks and explores the effects of category weight setting and attention mechanisms, aiming to find a more accurate model for recognizing forest fires. The paper first compares the performance of DenseNet121, InceptionV3, MobileNetV2, and ResNet50, finding that MobileNetV2 performs exceptionally well. Then, based on MobileNetV2, parameter tuning is carried out, determining the appropriate optimizer Adam, learning rate 0.001, and random seed 11. Subsequently, the paper explores category weight setting and attention mechanisms, ultimately finding that category weight setting has a significant effect, while the role of attention mechanisms is limited under the circumstances of this paper. Begin the abstract two lines below author names and addresses.