Cross-domain recognition methods of forest fire smoke images based on domain adversarial network

Objective This study suggests a cross-domain identification technique for forest fire smoke photos based on domain adversarial networks in order to address the issue of domain shift caused by spatiotemporal scene changes in forest fire smoke image detection. The approach attempts to address the issue of current methods’ inadequate recognition performance in cross-domain scenarios, particularly the difficulties in using them in complicated environments with varying backgrounds. It offers technical assistance for monitoring forest fires in intricate settings.

Method Firstly, this study introduced a conditional adversarial learning mechanism in training stage, which utilized the category information of forest fire smoke images to construct a conditional constraint network and enhanced the model’s ability to adapt to cross-domain features. Then, the domain invariant feature extraction module and the cross-domain feature alignment module were designed. In the domain-invariant feature extraction module, the pre-trained ResNet50 was used as infrastructure to generate discriminative domain-invariant features to solve the problem of feature distribution differences in cross-domain scenarios. In the cross-domain feature alignment module, the maximum mean difference metric and associated alignment constraints were fused to construct a dual constraint mechanism to optimize the feature spatial distribution. Finally, by fusing the conditional adversarial generative network and cross-domain feature alignment module, an end-to-end training framework was constructed to realize the cross-domain efficient recognition of forest fire smoke images.

Result The suggested approach’s average recognition accuracy in cross-domain forest fire smoke dataset under same experimental conditions was 92.39%, 0.94 percentage points higher than optimal baseline model (LEAD). Additionally, the key metrics (89.67% precision, 89.58% recall, and 89.54% F₁ score) were significantly better than the others, confirming that the approach presented in this paper was successful in enhancing the performance of forest fire smoke recognition. Experiments on multi-scene generalization demonstrated that even in the face of complicated meteorological interference, the model’s identification ability remained comparatively constant.

Conclusion In conclusion, the domain adversarial network-based cross-domain recognition technique for forest fire smoke images presented in this paper successfully enhanced the recognition performance of forest fire smoke images in various spatiotemporal scenarios and exhibited high robustness in complex environments. The approach offers fresh concepts and methods for studying forest fire smoke picture cross-domain identification, as well as a general reference value for additional research in related disciplines.