{'fraction_non_alphanumeric': 0.025365853658536587, 'fraction_numerical': 0.011707317073170732, 'mean_word_length': 4.930635838150289, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research and Implementation of Forest Fire Smoke Detection Based on ResNet Transfer Learning

Frequent forest fires damage the ecological environment and affect people's lives. Early monitoring of forest fire can significantly reduce the damage. Many methods have proposed by researchers for fire detection, but most of them are based on traditional image feature extraction. In the paper, we present a forest fire detection approach based on ResNet-18 transfer learning which can be used to train a model with a smaller dataset. First, image preprocessing methods were used to enlarge the dataset, which include random cropping and random erase. Secondly, the pre-trained ResNet-18 model is fine-tuned using the target train set. Experiments shows that the fine-tuned ResNet-18 model can effectively recognize the fire or smoke in the image, which got the accuracy of 94.33% on test set. Also, image preprocessing which contains random cropping and random erase can improve the performance of the fine-tuned ResNet-18 model.