Instance segmentation using semi-supervised learning for fire recognition

Fire disaster brings enormous danger to the safety of human life and property, and it is important to identify the fire situation in time through image processing technology. The current instance segmentation algorithms suffer from problems such as inadequate fire images and annotations, low recognition accuracy, and slow inference speed for fire recognition tasks. In this paper, we propose a semi-supervised learning-based fire instance segmentation method based on deep learning image processing technology. We used a lightweight version of the SOLOv2 network and optimized the network structure to improve accuracy. We propose a semi-supervised learning method based on fire features. To reduce the negative impact of error pseudo-labels on the model training, the pseudo-labels are matched by the color and morphological features of flames and smoke at the pseudo-label generation stage, and some images are screened for strong image enhancement before entering the next round of training for the student model. We further exploit the potential of the model with a limited dataset and improve the model accuracy without affecting the inference efficiency of the model. Experiments show that our proposed algorithm can successfully improve the accuracy of fire instance segmentation with good inference speed.