A Dynamic Feature-Based Method for Hybrid BlurredMultiple Object Detection in Manufacturing Processes

Abstract EN

Vision-based inspection has been applied for quality control and product sorting in manufacturing processes.

Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems.

Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing.

For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects.

Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images.

The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images.

The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods.

The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively.

The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples.

Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects.

The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.