Harnessing the power of AutoML: A comparative study of image recognition techniques for smoking detection

Abstract

As infrastructure continues to evolve, the significance of fire protection escalates. Many fires are caused by smoking in smoke-free areas, underscoring the necessity to promptly detect smoking activities in hazardous zones. In this scenario, image recognition emerges as a pivotal tool. The accuracy and efficiency of image recognition bear substantial implications for both academic and industrial sectors, and these aspects form the crux of our investigation. This study aims to compare the performance of image recognition techniques based on automatic machine learning with those of traditional methods such as YOLO. Our findings indicate that image recognition powered by automatic machine learning outperforms YOLO recognition in terms of efficiency and accuracy.