Development of a mechatronic sorting system for removing contaminants from wool

Automated visual inspection (AVI) systems have been extended to many fields, such as agriculture and the food, plastic and textile industries. Generally, most visual systems only inspect product defects, and then analyze and grade them due to the lack of any sorting function. This main reason rests with the difficulty of using the image data in real time. However, it is increasingly important to either sort good products from bad or grade products into separate groups usingAVI systems. This article describes the development of a mechatronic sorting system and its integration with a vision system for automatically removing contaminants from wool in real time. The integration is implemented by a personal computer, which continuously processes live images under the Windows 2000 operating system. The developed real-time sorting approach is also applicable to many other AVI systems

A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Efficient completeness inspection using real-time 3D color reconstruction with a dual-laser triangulation system

In this chapter, we present the final system resulting from the European Project \u201d3DComplete\u201d aimed at creating a low-cost and flexible quality inspection system capable of capturing 2.5D color data for completeness inspection. The system uses a single color camera to capture at the same time 3D data with laser triangulation and color texture with a special projector of a narrow line of white light, which are then combined into a color 2.5D model in real-time. Many examples of completeness inspection tasks are reported which are extremely difficult to analyze with state-of-the-art 2D-based methods. Our system has been integrated into a real production environment, showing that completeness inspection incorporating 3D technology can be readily achieved in a short time at low costs

Prediction of sedimentation and bank erosion due to the construction of Kahang Dam

River impoundments continue to cause changes to the hydrological regimes of its host river. Thus, assessment and development of tools for better understanding of the sediment dynamics and riverbank erosion downstream the dam will be of great benefit to researchers and policymakers. The present research employs the use of field techniques and estimation models to improve the (i) prediction of suspended sediment concentration, (ii) monitoring riverbank erosion, and (iii) development of Riverbank Erosion Index (RbEI) for downstream Kahang Dam. This research used the Artificial Neural Network (ANN) and ANN with Autoregressive (AR) (NNETAR) in predicting suspended sediment concentration using sediment concentration, discharge and water level as inputs. Similarly, erosion pins were installed on four transects to monitor the riverbank for thirteen months. The results obtained for sediment concentration prediction clearly show that the R2 for NNETAR (0.885) have better value compared to ANN (0.695) even though the relationship between discharge and sediment concentration was weak, it outperforms the ANN. While based on the sediment rating curve (SRC) results, the same pattern was exhibited where the R2 for NNETAR show a greater value than ANN and SRC with R2 values of 0.695 and 0.451, respectively. Based on the observed results of quantified riverbank erosion, the most active transect eroded 1.747 mm/yr- while 0.657 mm/yr- is the least eroded. furthermore, the result reveals the maximum and minimum sediment contribution to the fluvial system from riverbank eroded to be 0.00743 tonnes/yr and 0.00148 tonnes/yr respectively. Lastly, by using discharge and percentage soil composition (sand and clay), a RbEI was developed by the adopted Equation 4.7 to estimate the status of riverbank erosion of River Kahang. Moreover, five classifications of erosion status were proposed, which can be used to describe the status and severity of the riverbank erosion. In conclusion, the estimates by the RbEI is expected to serve as basis for analysing and adopting river stabilisation and restoration design, which will be of importance to dam operators in making informed decisions regarding early warnings on the riverbank stability. Also, reliable sediment concentration estimation will assist in the development of catchment sediment budget which will give an insight into the effect of situating a dam on a river in terms of sediment supply and riverbank erosio

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