Scale AI > Case Studies > Enhancing Log Scaling and Inventory Management with Scale Rapid

Enhancing Log Scaling and Inventory Management with Scale Rapid

Technology Category

Analytics & Modeling - Computer Vision Software
Sensors - Autonomous Driving Sensors

Applicable Industries

Equipment & Machinery

Applicable Functions

Facility Management
Quality Assurance

Use Cases

Inventory Management
Object Detection

Services

Testing & Certification

About The Customer

TimberEye is a company that provides a mobile application leveraging computer vision and LiDAR mapping technology to enable lumber suppliers and buyers to categorize and scale logs faster, more safely, and with better accuracy. The app is used by some of the largest log exporters in South America and in the US Southeast. It scales the logs, manages inventory, and also tracks the log’s journey from the forest, onto trucks, into loading facilities, and then on to ships overseas. TimberEye does this for hundreds of thousands of logs and countless shipments each month. As TimberEye expands into Canada, Europe, and New Zealand, their technology has the potential to not only accelerate and increase the accuracy of log scaling but also reduce injury rates and make it safer for the people working in these industries.

The Challenge

The TimberEye team faced a significant challenge in enhancing their mobile application's log scaling capabilities. The app, which uses computer vision and LiDAR mapping technology, was designed to help lumber suppliers and buyers categorize and scale logs faster, more safely, and with better accuracy. However, the team wanted to experiment with an instance segmentation model to further improve the app's scaling capabilities. The process of annotating images for segmentation proved to be a daunting task. TimberEye CEO and Founder Scott Gregg attempted to annotate a segmentation dataset on his own, but after three days and only 1,000 images labeled, he was burned out. The process was significantly more challenging and time-consuming than annotating images for object detection, requiring 100-200 mouse clicks per image instead of just 4. The team was overwhelmed and stuck, with only 5% of the dataset they needed to annotate complete.

The Solution

The TimberEye team turned to Scale Rapid as early beta testers to solve their annotation problem. Scale Rapid provided a fast, easy, and quality annotation solution. The team gave Scale very specific instructions for their use case, requiring them to create tight circular boundaries around the cross section of each log, and ignoring certain information in the periphery. Scale Rapid was able to deliver the data they needed in perfect shape within three days. The TimberEye team simply had to upload images, provide instructions, and follow the process until they received perfect data back. They only had to provide a bit of feedback for the early batches and do some audits. The rest of the time, they could watch as Scale Rapid knocked out thousands of perfect annotations in hours.

Operational Impact

The results from using Scale Rapid have been overwhelmingly positive for TimberEye. The company deals with a ton of corner cases that are very difficult to cover perfectly, including odd types of damage, mud, moisture, lighting, bark, shadows, and a whole number of variables that can throw off a vision model. With the segmentation results from Scale Rapid, TimberEye was able to apply a much greater level of consistency over a much broader set of the weird cases they encounter, with a lot less opinion in the final result. They no longer have to worry about how they will perform after a rainstorm, or with a log getting crushed at the bottom of a pile. Furthermore, their customers are delighted when they receive multi-hundred page packing lists, itemized at the log level, with images of every single log and its measurements. This level of data access is unprecedented in the industry.

Quantitative Benefit