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An optimized wood knots recognition scheme based on double detection

To improve the accuracy of wood knots recognition based on computer vision especially the live knot which got similar color with the surroundings, an optimized network architecture was proposed in this study. The model we created was consisted of two parallel networks to cope with two kinds wood defects: light and dark to improve the robustness of the model in color diversity. It was thought that the two well trained networks would be capable of recognizing of any kinds of wood defects between the two typical colors. To avoid repeated recognition of one object cause by the double detection, a decision making mechanism was established based on classification scores to determined the right category. The model was validated by using wood plates which contained 4 kinds of knots. It was shown that the double detection could successfully recognize live knots as well as other defects. The increased accuracy could be attributed to the using of parallel networks which enhanced the implicit feature of live knots. Instead of using complex CNN architecture like some classic models, the proposed method increased the accuracy of wood defects recognition by using parallel networks with relatively simple structure, which increased the speed of model thus was more suitable for industrial application.