Purpose – When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed. Design/methodology/approach – First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects. Findings – Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments. Originality/value – A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

No abstract

No abstract

No abstract

No abstract

Purpose This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy. Design/methodology/approach A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing. Findings Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy. Originality/value First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

Most astronomical source classification algorithms based on photometric data struggle to classify sources as quasars, stars and galaxies reliably. To achieve this goal and build a new SDSS photometric catalog in the future, we apply a deep learning source detection network built on YOLO v4 object detection framework to detect sources and design a new deep learning classification network named APSCnet (astronomy photometric source classification network) to classify sources. In addition, a photometric background image generation network is applied to generate background images in the process of data sets synthesis. Our detection network obtains a mAP score of 88.02 when IOU=0.5. As for APSCnet, in a magnitude range with 14 ∼ 25, we achieve a precision of 84.1% at 93.2% recall for quasars, a precision of 94.5% at 84.6% recall for stars and a precision of 95.8% at 95.1% recall for galaxies; and in a magnitude range with less than 20, we achieve a precision of 96.6% at 94.7% recall for quasars, a precision of 95.7% at 97.4% recall for stars and a precision of 98.9% at 99.2% recall for galaxies. We have proved the superiority of our algorithm in the classification of astronomical sources through comparative experiments between multiple sets of methods. In addition, we also analyzed the impact of PSF on the classification results. These technologies may be applied to data mining of the next generation sky surveys, such as LSST, WFIRST, and CSST et al.

The pollution of Cadmium (Cd) species in natural water has attracted more and more attention due to its high cumulative toxicity. In the search for improved removal of cadmium from contaminated water, we characterized uptake on a recently identified nanomaterial (SiO2-Mg(OH)2) obtained by subjecting sepiolite to acid-base modification. The structural characteristics of SiO2-Mg(OH)2 were analyzed by means of SEM-EDS, Fourier Transform Infra-Red Spectroscopy (FTIR) and Powder X-ray Diffraction (PXRD). Static adsorption experiments were carried out to evaluate the effect of contact time, temperature, amount of adsorbent, and pH-value on the adsorption of Cd(II) by SiO2-Mg(OH)2. The results show that the pore structure of SiO2-Mg(OH)2 is well developed, with specific surface area, pore size and pore volume increased by 60.09%, 16.76%, and 43.59%, respectively, compared to natural sepiolite. After modification, the sepiolite substrate adsorbs Cd(II) following pseudo-second-order kinetics and a Langmuir surface adsorption model, suggesting both chemical and physical adsorption. At 298 K, the maximum saturated adsorption capacity fitted by Sips model of SiO2-Mg(OH)2 regarding Cd(II) is 121.23 mg/g. The results show that SiO2-Mg(OH)2 nanocomposite has efficient adsorption performance, which is expected to be a remediation agent for heavy metal cadmium polluted wastewater.