Going Against the Grain: How Lucidyne Is Revolutionizing Lumber Grading with Deep Learning
Talk about a knotty problem.
Lucidyne Technologies has been using AI since the late 1980s to detect defects in lumber products.
But no matter how much technology it’s employed, finding imperfections in wood boards — a process that’s critical to categorizing lumber and thus maximizing its value — has remained a challenge.
“This isn’t like being in a factory and scanning cogs. These are all like snowflakes,” said Patrick Freeman, CTO of the small, Corvallis, Oregon-based company. “There’s never been a knot that looks like another one.”
It’s a job tailor-made for AI, and Lucidyne has jumped in with both feet by building a cutting-edge scanning system for lumber mills that’s powered by GPU-enabled deep learning.
With lumber flying through at speeds of up to 35 mph, the company’s GradeScan system — which physically resembles a mashup of an assembly line station and an MRI machine — scans two boards a second. It detects and collects visual data on 70 different types of defects, such as knots, fire scars, pitch pockets and seams.
It then applies a deep learning model trained on a combination of NVIDIA GPUs, with a dataset of hundreds of thousands of scanned boards across 16 tree species, all of which have been classified by a team of lumber-grading experts.
To generate the most revenue, the model’s underlying algorithm determines the optimal way to cut each board — navigating around defects measuring as little as 8/1,000th of an inch. Those instructions are then sent to the mill’s saws.
Each mill’s findings are fed back into Lucidyne’s dataset, continuously improving the accuracy and precision of its deep learning model. Thus, there’s no end to how much mills will be able to learn about the lumber they’re milling.
Unprecedented Accuracy and Precision
A typical scanning application might involve categorizing lumber into one of six grade types, with grade 1 being the most valuable, for example. After scanning a 20-foot board, Lucidyne’s system might determine that the best cut will remove a 2-foot defective section near the center, leaving two 8-foot grade 1 and 2 sections on either side, and an additional 2-foot section of trim, which might be sold to a sawdust manufacturer.
This level of detail separates Lucidyne from the competition by enabling mills to drastically improve the precision of their lumber-grading efforts.
“Going to deep learning has allowed us to be a lot more accurate, and our customers produce packs that are 2 percent below or above grade,” said Dan Robin, Lucidyne’s software engineering manager. “No one else is coming even close to that.”
Lucidyne started deploying GradeScan systems, powered by its Perceptive Sight software, in 2017, with each unit performing inference on NVIDIA P4 GPUs. The company is now deploying systems with newer NVIDIA T4 GPUs.
Freeman said the new system is delivering 16x the data processing speed, and at a higher image resolution to boot.
The upshot is that Lucidyne’s decision to travel a deep learning path toward increasingly detailed identification of defects has paid off exactly as it hoped.
Raising the Bar
“We wanted to up our game,” said Freeman. “We sought to improve our accuracy on currently detected defects, to correctly classify defects we had never been able to call before, while at the same time delivering more timely solutions and to a larger customer base.”
To that end, the company is working with NVIDIA to develop customized software that extends fine-grain inferencing capabilities using semantic segmentation.
In the meantime, Lucidyne is riding every wave of increased computing power to zoom in on smaller and more subtle defects. It has recently begun grading redwood, which is much harder to scan because of its color variations. It’s also looking to expand into hardwoods and eventually hopes to tackle other challenges faced by mills.
All of this innovation has Lucidyne’s technical leaders feeling that they’re onto something bigger. As a result, they have an eye on disrupting other sectors where inspection of organic materials is involved.
Said Freeman, “What we’re doing that we think is unique is taking industrial deep learning inspection to the next level.”
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