Adaptive learning method for multipleobject detection in manufacturing

Abstract

In manufacturing, vision-based inspections are effective nondestructive methods for implementing object discrimination. To minimize inspection times, multiple-object discrimination must be implemented for one inspection image. However, multiple-object inspections are difficult to perform in manufacturing because of the lack of distinct objects in inspection images. In addition, inspectors might fail to use multiple sensing devices when concurrently detecting objects. This article proposes a novel multiple-object sensing system that incorporates a local-Adaptive-region-growing-based learning method for adaptively segmenting multiple-camera images for multiple-object discrimination. The proposed local-Adaptive- region-growing method and support vector machine-based discrete wavelet transform can effectively classify multiple objects in the local subregions of inspection images. The proposed system bridges the gap between sensing devices and inspectors for solving problems encountered when concurrently using multiple sensing devices. The learning method yielded more favorable results than existing inspection methods have. Furthermore, a standard test series was developed for quantitatively comparing inspection methods.