Part-based adaptive detection of workpieces using differential evolution

In many industrial applications, detection of workpieces is the prerequisite of the subsequent operations such as automatic grasping and assembly tasks. However, the detection of workpieces under challenging conditions such as occlusion and cluttered background is still an open problem, which needs better solutions and further investigations. In this paper, a part-based adaptive detection approach is proposed to deal with abovementioned problems. The whole workpiece template is automatically divided into multiple subtemplates, which are equipped with adjustable weights adjusted according to their discriminative abilities. Then the weight adjustment process and the object localization process are finally embedded in an optimization framework - Differential Evolution (DE), which finally leads to the detection of workpieces. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm under challenging conditions.