Abstract

In this article, we describe a system for machine vision–based lumber strength prediction. The system utilizes images taken from all four sides of pinewood boards. Those images are further divided into small subareas, and the local gradients inside each area are used to calculate the local grain direction. Together, these directions form the grain direction map. The grain direction map and knot features are used to predict the breaking strength of the board. Because of the high speed of production lines, we also present a parallel general-purpose graphics processing unit (GPGPU) implementation of the method to achieve real-time performance using low-cost hardware. We describe the challenges of the design on a GPU compared with a traditional central processing unit implementation. Most of the modern sawmills already have multiple camera systems in use, making the camera-based strength prediction extremely cost effective. In our experiments, an r2 value of 0.63 was obtained between the measured strength attributes of the board and our strength prediction coefficient. The ground truth for the breaking strength was measured using destructive 3-point bending tests. Using a regular desktop computer, the described system achieves a throughput of over 50 Mpixels/s. For the parallel implementation, we provide qualitative evaluation of the results and a comparison of the computational speed on several platforms.

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