{'fraction_non_alphanumeric': 0.013888888888888888, 'fraction_numerical': 0.0016339869281045752, 'mean_word_length': 5.447368421052632, '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}}
An Intelligent Wildfire Detection Approach through Cameras Based on Deep Learning

Fire is one of the most serious disasters in the wild environment such as mountains and jungles, which not only causes huge property damage, but also may lead to the destruction of natural ecosystems and a series of other environmental problems. Considering the superiority and rapid development of computer vision, we present a novel intelligent wildfire detection method through video cameras for preventing wildfire hazards from becoming out of control. The model is improved based on YOLOV5S architectures. At first, we realize its lightweight design by incorporating the MobilenetV3 structure. Moreover, the improvement of detection accuracy is achieved by further improving its backbone, neck, and head layers. The experiments on a dataset containing a large number of wild flame and wild smoke images have demonstrated that the novel model is suitable for wildfire detection with excellent detection accuracy while meeting the requirements of real-time detection. Its wild deployment will help detect fire at the very early stage, effectively prevent the spread of wildfires, and therefore significantly contribute to loss prevention.