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In recent years, with the rapid development of economy and urbanization, a large number of parks have been built all over the world. The number of fire accidents occurring in the parks is also increasing, resulting in tremendous casualties and property damage. These parks usually cover multiple sub-scenes, such as houses, vehicles and greenery, and each sub-scene has its own unique fire accident features. Existing fire detection methods cannot meet the diversity of subscenes in the park. To improve the detection capability of each sub-scene model, we propose a cloud edge collaboration framework based on transfer learning for training fire detection models with multiple sub-scenes in a park. The framework has two phases: initialization phase and update phase, using the rich data and powerful computing power in the cloud to assist the edge nodes with limited resources to train the models. First, the cloud integrates the fire data of all edge nodes for model training to obtain all fire features, and then each edge node uses its own data set to fine-tune the cloud-trained model after freezing some parameters. In this paper, we construct a fire image data set with multiple subscenes in a park and use the YOLOv5s model for experiments, and the experimental results show that the framework can improve the mAP of each edge node model. This paper provides a novel approach for fire detection, which is of positive significance for fire prevention and control.
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