A vision-based system for inspecting painted slates

Purpose – This paper describes the development of a novel automated vision system used to detect the visual defects on painted slates. Design/methodology/approach – The vision system that has been developed consists of two major components covering the opto-mechanical and algorithmical aspects of the system. The first component addresses issues including the mechanical implementation and interfacing the inspection system with the development of a fast image processing procedure able to identify visual defects present on the slate surface. Findings – The inspection system was developed on 400 slates to determine the threshold settings that give the best trade-off between no false positive triggers and correct defect identification. The developed system was tested on more than 300 fresh slates and the success rate for correct identification of acceptable and defective slates was 99.32 per cent for defect free slates based on 148 samples and 96.91 per cent for defective slates based on 162 samples. Practical implications – The experimental data indicates that automating the inspection of painted slates can be achieved and installation in a factory is a realistic target. Testing the devised inspection system in a factory-type environment was an important part of the development process as this enabled us to develop the mechanical system and the image processing algorithm able to perform slate inspection in an industrial environment. The overall performance of the system indicates that the proposed solution can be considered as a replacement for the existing manual inspection system. Originality/value – The development of a real-time automated system for inspecting painted slates proved to be a difficult task since the slate surface is dark coloured, glossy, has depth profile non-uniformities and is being transported at high speeds on a conveyor. In order to address these issues, the system described in this paper proposed a number of novel solutions including the illumination set-up and the development of multi-component image-processing inspection algorithm

A Contrast-Based Approach to the Identification of Texture Faults

Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max-min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max -min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems

Automation of painted slate inspection

This thesis is concerned with the problem of how to detect visual defects on painted slates using an automated visual inspection system. The vision system that has been developed consists of two major components. The first component addresses issues such as the mechanical implementation and interfacing the inspection system with the optical and sensing equipment whereas the second component involves the development of an image processing algorithm able to identify the visual defects present on the slate surface. The visual defects can be roughly classified into two distinct categories. In this way, substrate faults occur when the slate is not fully formed or has excess material whilst paint faults describe a slate of uneven colour or gloss level. A key element in successfully imaging the slate surface defects is the illumination set-up. After extensive testing, an effective collimated lighting topology was selected and is described in detail. Imaging the slate surface was challenging because it is dark coloured, glossy and has depth profile non-uniformities. A four component image processing algorithm was designed to detect the range of defect types. The constituent components are global mean threshold, adaptive signal threshold, labelling, edge detection and labelling. Having proven a solution on the laboratory test bed, a prototype conveyor-based inspection system was assembled in order to replicate a factory-style environment. Robustness tests were performed on 400 slates and a 97% success rate was achieved. This thesis is concluded with a discussion on the feasibility of progressing this project to installation on an automated production line