FireMatch: A Semi-Supervised Video Fire Detection Network Based on
  Consistency and Distribution Alignment
Qinghua Lin Zuoyong Li Kun Zeng Haoyi Fan Wei Li Xiaoguang Zhou
Deep learning techniques have greatly enhanced the performance of fire
detection in videos. However, video-based fire detection models heavily rely on
labeled data, and the process of data labeling is particularly costly and
time-consuming, especially when dealing with videos. Considering the limited
quantity of labeled video data, we propose a semi-supervised fire detection
model called FireMatch, which is based on consistency regularization and
adversarial distribution alignment. Specifically, we first combine consistency
regularization with pseudo-label. For unlabeled data, we design video data
augmentation to obtain corresponding weakly augmented and strongly augmented
samples. The proposed model predicts weakly augmented samples and retains
pseudo-label above a threshold, while training on strongly augmented samples to
predict these pseudo-labels for learning more robust feature representations.
Secondly, we generate video cross-set augmented samples by adversarial
distribution alignment to expand the training data and alleviate the decline in
classification performance caused by insufficient labeled data. Finally, we
introduce a fairness loss to help the model produce diverse predictions for
input samples, thereby addressing the issue of high confidence with the
non-fire class in fire classification scenarios. The FireMatch achieved an
accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively.
The experimental results demonstrate that the proposed method outperforms the
current state-of-the-art semi-supervised classification methods.