Large-Space Fire Detection Technology: A Review of Conventional Detector Limitations and Image-Based Target Detection Techniques

With the rapid development of large-space buildings, their fire risk has become increasingly prominent. Conventional fire detection technologies are often limited by spatial height and environmental interference, leading to false alarms, missed detections, and delayed responses. This paper reviews 83 publications to analyze the limitations of conventional methods in large spaces and highlights the advantages of and current developments in image-based fire detection technology. It outlines key aspects such as equipment selection, dataset construction, and target recognition algorithm optimization, along with improvement directions including scenario-adaptive datasets, model enhancement, and adaptability refinement. Research demonstrates that image-based technology offers broad coverage, rapid response, and strong anti-interference capability, effectively compensating for the shortcomings of conventional methods and providing a new solution for early fire warning in large spaces. Finally, future prospects are discussed, focusing on environmental adaptability, algorithm efficiency and reliability, and system integration, offering valuable references for related research and applications.