  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.
