Object recognition-based automated inspection system for hose assembly

Computer vision–based inspection has been widely applied in automated quality inspection of lots of products. Extant research pays more attention to non-deformable parts, but little to deformable parts, such as hoses, which are popularly used in mechanical and electronic products to transport liquid because of advantages of deformability. Hence, a problem of automated inspection of hose assembly was defined and divided into four subproblems of online inspection, offline data analysis, improvement of inspection, and online quality control. Focusing on the first subproblem, a computer vision–based system was developed to deal with online inspection. Concerning the crucial unit of the system, objection recognition, features of hoses, and related fasteners were analyzed to present four propositions on shape and color constraints, and then a series of systematic techniques both in the spatial and spectral domains were put forward and discussed to perform image processing, object extraction, and feature recognition. Experiments show that average accuracies of object recognition and inspection are over 92% and even up to 100%, respectively, within an average running time of less than 30 s, which meets requirements of online inspection both on accuracy and efficiency. Since the system structure and corresponding methods are task-irrelevant, it can be generalized to figure out other inspection applications involving rotational parts.

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