Know your chemistry set: Exploring chemical laboratory object detection using YOLOv8 and YOLOv9

Abstract

Image object detection (IOD) has proven its usefulness from diagnosing critical diseases from medical image analysis to pedestrian recognition in autonomous vehicle tracking. Considering the potential applications of object detection in real-life scenarios, various deep learning-based algorithms have been used in recent years. However, one unexplored sector of object detection is its application in critical environments like a chemical lab. Automatic apparatus/chemical reagent/machine detection can lessen the effect of chemical hazards in these environments as well as can be used to ensure efficient usage of laboratory resources. In this study, the potential of you only look once (YOLO) has been explored for the detection of chemical apparatus from a comprehensive image dataset. The study was validated against a dataset of 5078 images containing 7 different most commonly used chemistry laboratory apparatus that are used chemical laboratory. Our experimentation demonstrates state-of-the-art performance on the detection of objects with an impressive mAP of 0.818 and 0.865 using YOLOv8 and YOLOv9 architectures respectively.