木材表面缺陷检测是木材行业中一项至关重要的任务。准确地检测木材表面缺陷从 而可以准确分级,可以帮助生产商提高产品质量,合理分级,以及降低生产成本。然而 木材表面缺陷检测并不容易,因为木材的表面缺陷种类繁多,形状各异,同时还需要应 对不同光照条件下的挑战。 同时,分选过程会根据上下游生产工艺的不同而有所差异,存在标准不统一,分选 效果差的问题。 为了解决这一问题,本研究设计一种木材表面缺陷检测与分选方法,将深度学习目 标检测技术与基于贝叶斯神经网络的瑕疵判别、以及贝叶斯分类器相结合,由基于深度 学习的目标检测模型完成缺陷检测,根据目标检测模型给出的缺陷位置和类别,构建贝 叶斯网络,依据缺陷种类、缺陷数量、缺陷位置,实现对木材的等级分类,从而最终实 现木材分选。
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