Among various calamities, conflagrations stand out as one of the most-prevalent and -menacing adversities, posing significant perils to public safety and societal progress. Traditional fire-detection systems primarily rely on sensor-based detection techniques, which have inherent limitations in accurately and promptly detecting fires, especially in complex environments. In recent years, with the advancement of computer vision technology, video-oriented fire detection techniques, owing to their non-contact sensing, adaptability to diverse environments, and comprehensive information acquisition, have progressively emerged as a novel solution. However, approaches based on handcrafted feature extraction struggle to cope with variations in smoke or flame caused by different combustibles, lighting conditions, and other factors. As a powerful and flexible machine learning framework, deep learning has demonstrated significant advantages in video fire detection. This paper summarizes deep-learning-based video-fire-detection methods, focusing on recent advances in deep learning approaches and commonly used datasets for fire recognition, fire object detection, and fire segmentation. Furthermore, this paper provides a review and outlook on the development prospects of this field.

The coupling and coordination between green finance (GF) and economic resilience (ER) are the foundation of sustainable economic development. This paper uses the panel data of 30 provinces (autonomous regions and municipalities) in mainland China from 2011 to 2021 to calculate the comprehensive development level of the two systems by the entropy weight method. At the same time, we analyze the spatiotemporal evolution characteristics of the coupling coordination degree of the two systems by using the coupling coordination degree model, kernel density curve, spatial autocorrelation model, and Markov transition matrix. The results show that (1) the development level of ER increased steadily while that of GF fluctuated. The coupling coordination degree of the two systems shows an increasing trend. (2) The coupling coordination level of the two systems presents a spatial gradient pattern of “East > Middle > West”. (3) The level of coupling coordination has an obvious spatial correlation. (4) The coupling coordination level in our country remains stable in the future, and there is a possibility of transition to a higher level. The research of this paper provides valuable enlightenment for implementing a sustainable development strategy in China.