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

Intensity-based image registration using multiple distributed agents

Image registration is the process of geometrically aligning images taken from different sensors, viewpoints or instances in time. It plays a key role in the detection of defects or anomalies for automated visual inspection. A multiagent distributed blackboard system has been developed for intensity-based image registration. The images are divided into segments and allocated to agents on separate processors, allowing parallel computation of a similarity metric that measures the degree of likeness between reference and sensed images after the application of a transform. The need for a dedicated control module is removed by coordination of agents via the blackboard. Tests show that additional agents increase speed, provided the communication capacity of the blackboard is not saturated. The success of the approach in achieving registration, despite significant misalignment of the original images, is demonstrated in the detection of manufacturing defects on screen-printed plastic bottles and printed circuit boards

Support Vector Machine classification of strong gravitational lenses

The imminent advent of very large-scale optical sky surveys, such as Euclid and LSST, makes it important to find efficient ways of discovering rare objects such as strong gravitational lens systems, where a background object is multiply gravitationally imaged by a foreground mass. As well as finding the lens systems, it is important to reject false positives due to intrinsic structure in galaxies, and much work is in progress with machine learning algorithms such as neural networks in order to achieve both these aims. We present and discuss a Support Vector Machine (SVM) algorithm which makes use of a Gabor filterbank in order to provide learning criteria for separation of lenses and non-lenses, and demonstrate using blind challenges that under certain circumstances it is a particularly efficient algorithm for rejecting false positives. We compare the SVM engine with a large-scale human examination of 100000 simulated lenses in a challenge dataset, and also apply the SVM method to survey images from the Kilo-Degree Survey.Comment: Accepted by MNRA

Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments

Automatic Color Inspection for Colored Wires in Electric Cables

In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions

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

Spectroscopic detection of quasars in the 2dF Galaxy Redshift Survey

The 100,000 spectra from the 2 degree Field Galaxy Redshift Survey (2dFGRS) in the 100k Public Data Release represent the largest single compilation of galaxy spectra available. By virtue of its sheer size and the properties of the photometric catalogue that defines the sample, the 2dFGRS is expected to contain a number of potentially interesting objects other than galaxies. A search of the spectra in the 100k Data Release results in a census of 55 candidate high-redshift (z > 0.3) quasars. One additional 2dFGRS spectrum of a low-redshift galaxy shows an apparent anomalous broad emission feature perhaps indicating the presence of a gravitationally lensed quasar. These objects have been identified primarily using two automated routines that we have developed specifically for this task, one of which uses a matched filter and the other a wavelet transform. A number of the quasar images possess complicated morphologies, suggesting the presence of either diffuse foreground objects along the line-of-sight or very nearby point sources. The quasar catalogue will form a target list for future absorption and lensing studies, as well as providing an assessment of the loss of quasars with non-stellar images from the companion 2dF QSO Redshift Survey.Comment: Latex 13 pages, 8 figures. Accepted for publication in MNRA