一种面向机器人分拣的杂乱工件视觉检测识别方法

In order to sort out the target workpieces meeting the specifications from the clutter workpieces on the conveyor belt, a novel detection and recognition method based on the aggregated segmentation of multi-frame workpiece images is proposed. Firstly, the method obtains the workpiece images by an industrial high-precision camera, and uses the watershed algorithm to successfully separate clustered workpiece images. Then, basing on the shape features of the workpieces, the classification of workpiece images is performed by using classification and regression trees (CART). Furthermore, by applying histogram backprojection and kernel density estimation, object masks of one tracked workpiece from multiple frames are combined into a refined single one, so as to accurately measure the size of the workpieces. Finally, to achieve robot sorting, the parameters of robot hand-eye calibration are combined to obtain the pose of the target workpieces.