Journal of Pioneering Artificial Intelligence Research

Abstract

Early warning of forest, grassland, and open-area urban fires relies on detecting flames or smoke at the initial stage of a fire. Accurate and efficient identification of early-stage fires is crucial for reducing casualties, protecting property, and minimizing ecological damage. Today, computer vision and deep learning technologies are the preferred tools for achieving this function. Among many models, the YOLO family is particularly popular because it can perform image and video classification and analysis efficiently and in real time, and its relatively low deployment requirements allow for rapid promotion in engineering applications. As noted by Ultralytics, YOLO26 goes further by providing a truly end-to-end detection without the need for an external NMS step. It also abandons Distribution Focal Loss in favor of Progressive Loss, while introducing Small-Target-Aware Label Assignment and MuSGD, and offers better support for running on mainstream devices. These functions meet the monitoring requirements for various wildfire and open-area scenarios. This paper is a review and system framework study. It utilizes the official YOLO26 description, representative YOLO literature, and previous research on flame, smoke, and wildfire detection to propose a YOLO26-based wildfire flame detection software framework. The paper reviews related work, describes the characteristics of YOLO26, provides a qualitative comparison of early and mainstream versions including YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLO11, and outlines the system architecture, preliminary use scenarios, and validation steps.We still need to extensively use public datasets, forest monitoring videos, drone images, and experiments targeting specific scenarios for testing, ultimately providing a complete conclusion.