Targeted application of deep learning to automated visual inspection equipment

An automated visual inspection system comprising:

• a line scan camera;
• conveying means for orienting a vessel containing a liquid sample such that the line scan camera has a side view of an edge of a stopper of the vessel;
• spinning means for spinning the vessel;
• and
• processing means for
• causing the line scan camera to capture, while the spinning means spins the vessel, a plurality of images of the edge of the stopper, wherein each image of the plurality of images is a side view image and corresponds to a different rotational position of the vessel,
• wherein the line scan camera is angled upward relative to the horizontal plane to match or approximate a slope of the stopper,
• generating, based on at least the plurality of images, a two-dimensional image of the edge of the stopper of the vessel, and
• processing, by executing an inference model that includes a trained neural network, pixels of the two-dimensional image to generate output data indicative of whether the liquid sample is acceptable, wherein the output data is indicative of whether the liquid sample includes one or more objects of a particular type or types, and wherein the trained neural network is configured to discriminate between gas-filled bubbles and particles in the liquid sample.

An automated visual inspection system comprising:

• a line scan camera;
• sample positioning hardware configured to orient a vessel containing a liquid sample such that the line scan camera has a side view of an edge of a stopper of the vessel, and to spin the vessel while so oriented;
• and
• a memory storing instructions that, when executed by one or more processors, cause the one or more processors to
• cause the line scan camera to capture, while the vessel is spinning, a plurality of images of the edge of the stopper, wherein each image of the plurality of images is a side view image and corresponds to a different rotational position of the vessel,
• wherein the line scan camera is angled upward relative to the horizontal plane to match or approximate a slope of the stopper,
• generate, based on at least the plurality of images, a two-dimensional image of the edge of the stopper of the vessel, and
• process, by executing an inference model that includes a trained neural network, pixels of the two-dimensional image to generate output data indicative of whether the liquid sample is acceptable, wherein the output data is indicative of whether the liquid sample includes one or more objects of a particular type or types, and wherein the trained neural network is configured to discriminate between gas-filled bubbles and particles in the liquid sample.