I am interested in learning more about the capabilities of AI, one of my ideas with practical functionality is using images of the rear of a log hauling truck to measure the individual logs using AI. The diameter and length of the log determine the board foot of that log. The diameter is the variable, with most lengths being the same 8ft or 16ft length. The AI would have to measure the diameter usually in 1-inch increments. I assume you would have to train the AI using pictures of logs that were manually measured. The images would mostly be straight on rear shots, not much off angle. However not every tree grows perfectly round so it will need to be able to measure those to maximize board feet.

Using AI long term it would be interesting if it could suggest the best way to cut the log gaining the most lumber board feet from the log.

What would be used to develop something like this? Obviously some type of image recognition with learning.


A good machine learning object detector, would be helpful in finding the logs but classical image processing would be required to accurately find the log profiles. Neural networks are generally not used for computing geometric dimensions. In addition, you will need to have a calibration method so that you can convert pixels to some distance unit (e.g. inches, centimeters, etc.).

The Timbeter app seems to do what you need (see below).

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This app (see below) is interesting although it doesn't solve your problem. It appears good at counting but finding the profiles to within an inch is a bigger problem.

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This blog "HOW MANY LOGS CAN A WOODCHUCK COUNT?" goes over the image processing challenges of counting logs. The author doesn't completely solve the problem.

There is a post on Stackoverflow, How to count white object on Binary Image? which makes an attempt.

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