A Comparative Performance Evaluation of YOLO-Type Detectors on a New Open Fire and Smoke Dataset

Abstract

The paper introduces a new FireAndSmoke open dataset comprising over 22,000 images and 93,000 distinct instances compiled from 1200 YouTube videos and public Internet resources. The scenes include separate and combined fire and smoke scenarios and a curated set of difficult cases representing real-life circumstances when specific image patches may be erroneously detected as fire/smoke presence. The dataset has been constructed using both static pictures and video sequences, covering day/night, indoor/outdoor, urban/industrial/forest, low/high resolution, and single/multiple instance cases. A rigorous selection, preprocessing, and labeling procedure has been applied, adhering to the findability, accessibility, interoperability, and reusability specifications described in the literature. The performances of the YOLO-type family of object detectors have been compared in terms of class-wise Precision, Recall, Mean Average Precision (mAP), and speed. Experimental results indicate the recently introduced YOLO10 model as the top performer, with 89% accuracy and a mAP@50 larger than 91%.

Keywords

• fire and smoke detection
• mAP
• YOLO object detectors
• real-time operation