Machine vision for vial positioning detection toward the safe automation of material synthesis

TL;DR

This paper introduces DenseSSD, a deep learning object detector that significantly improves vial position detection accuracy in complex, noisy environments, enhancing safety in automated chemical laboratories.

Contribution

The paper presents DenseSSD, a novel deep learning detector that outperforms existing models in accuracy and robustness for vial detection in automated material synthesis.

Findings

DenseSSD achieved over 95% mAP on complex vial datasets.

DenseSSD maintained high accuracy despite environmental variations.

DenseSSD enables safer and more flexible automation in chemical labs.

Abstract

Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors. Object detection techniques can play vital roles in addressing these safety issues; however, state-of-the-art detectors, including single-shot detector (SSD) models, suffer from insufficient accuracy in environments involving complex and noisy scenes. With the aim of improving safety in a surveillance-free laboratory, we report a novel deep learning (DL)-based object detector, namely, DenseSSD. For the foremost and frequent problem of detecting vial positions, DenseSSD achieved a mean average precision (mAP) over 95% based on a complex dataset involving both empty and solution-filled vials, greatly exceeding those of conventional detectors; such high precision is critical to minimizing failure-induced accidents. Additionally, DenseSSD was observed to be highly insensitive to the environmental changes, maintaining its high precision under the variations of solution colors or testing view angles. The robustness of DenseSSD would allow the utilized equipment settings to be more flexible. This work demonstrates that DenseSSD is useful for enhancing safety in an automated material synthesis environment, and it can be extended to various applications where high detection accuracy and speed are both needed.