DSN-BR-Based Online Inspection Method and Application for Surface Defects of Pharmaceutical Products in Aluminum-Plastic Blister Packages

Ensuring high product quality is of paramount importance in pharmaceutical drug manufacturing,as it is subject to rigorous regulatory practices.This study presents a research focused on the development of an on-line detection method and system for identifying surface defects in pharmaceutical products packaged in aluminum-plastic blisters.Firstly,the aluminum-plastic blister packages exhibit multi-scale features and inter-class indistinction.To address this,the deep semantic network with boundary refinement(DSN-BR)model is proposed,which leverages semantic segmentation domain knowledge,to accurately segment the defects in pixel level.Additionally,a specialized image acquisition module that minimizes the impact of ambient light is established,ensuring high-quality image capture.Finally,the image acquisition module,image detection module,and data management module are designed to construct a comprehensive online surface defect detection system.To validate the effectiveness of our approach,we employ a real dataset for instance verification on the implemented system.The experimental results substantiate the outstanding performance of the DSN-BR,achieving the mean intersection over union(MIoU)of 90.5%.Furthermore,the proposed system achieves an inference speed of up to 14.12 f/s,while attaining an F1-Score of 98.25%.These results demonstrate that the system meets the actual needs of the enterprise and provides theoretical and methodological support for intelligent inspection of product surface quality.By standardizing the control process of pharmaceutical manufacturing and improving the management capability of the manufacturing process,our approach holds significant market application prospects.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Surface Defect Detection and Evaluation Method of Large Wind Turbine Blades Based on an Improved Deeplabv3+Deep Learning Model

The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.

Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy

A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.

Application of multi-scale feature extraction to surface defect classification of hot-rolled steels

Feature extraction is essential to the classification of surface defect images. The defects of hot-rolled steels distribute in different directions. Therefore, the methods of multi-scale geometric analysis （MGA） were employed to decompose the image into several directional subba^ds at several scales. Then, the statistical features of each subband were calculated to produce a high-dimensional feature vector, which was reduced to a lower-dimensional vector by graph embedding algorithms. Finally, support vector machine （SVM） was used for defect classification. The multi-scale feature extraction method was implemented via curvelet transform and kernel locality preserving projections （KLPP）. Experiment results show that the proposed method is effective for classifying the surface defects of hot-rolled steels and the total classification rate is up to 97.33%.

Building surface defects by doping with transition metal on ultrafine TiO_2 to enhance the photocatalytic H_2 production activity

Inefficient charge separation and limited light absorption are two critical issues associated with high‐efficiency photocatalytic H2production using TiO2.Surface defects within a certain concentration range in photocatalyst materials are beneficial for photocatalytic activity.In this study,surface defects(oxygen vacancies and metal cation replacement defects)were induced with a facile and effective approach by surface doping with low‐cost transition metals(Co,Ni,Cu,and Mn)on ultrafine TiO2.The obtained surface‐defective TiO2exhibited a3–4‐fold improved activity compared to that of the original ultrafine TiO2.In addition,a H2production rate of3.4μmol/h was obtained using visible light(λ>420nm)irradiation.The apparent quantum yield(AQY)at365nm reached36.9%over TiO2‐Cu,significantly more than the commercial P25TiO2.The enhancement of photocatalytic H2production activity can be attributed to improved rapid charge separation efficiency andexpanded light absorption window.This hydrothermal treatment with transition metal was proven to be a very facile and effective method for obtaining surface defects.

Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform （FFF） and sum of valid pixels （SVP）, and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.

Surface defects in 4H-SiC homoepitaxial layers

Although a high-quality homoepitaxial layer of 4H‑silicon carbide(4H-SiC)can be obtained on a 4°off-axis substrate using chemical vapor deposition,the reduction of defects is still a focus of research.In this study,several kinds of surface defects in the 4H-SiC homoepitaxial layer are systemically investigated,including triangles,carrots,surface pits,basal plane dislocations,and step bunching.Themorphologies and structures of surface defects are further discussed via optical microscopy and potassium hydroxide-based defect selective etching analysis.Through research and analysis,we found that the origin of surface defects in the 4H-SiC homoepitaxial layer can be attributed to two aspects:the propagation of substrate defects,such as scratches,dislocation,and inclusion,and improper process parameters during epitaxial growth,such as in-situ etch,C/Si ratio,and growth temperature.It is believed that the surface defects in the 4H-SiC homoepitaxial layer can be significantly decreased by precisely controlling the chemistry on the deposition surface during the growth process.

Surface-defective FeS2 for electrochemical NH3 production under ambient conditions

In the viewpoint of ammonia economy,electrochemical N2 reduction reaction(NRR)under mild condition is a very promising approach for sustainable development.By virtue of robust activity and low cost,transition-metalbased materials become one kind of the most attractive electrocatalysts in realizing ammonia synthesis to the industrial level.However,the investigation related to NRR electrocatalysts still mainly rely on costly substance or fabrication process,which greatly restrict their large-scale applications.In this work,a simple fabricated FeS2 electrode is adopted as NRR catalysts.The abundant surface defects due to the existence of Cr element,as well as the synergistic effect between FeS2 crystal planes provided excellent electrocatalytic performance with a high NH3 yield rate(11.5μg h^-1mg^-1 Fe)and Faradaic efficiency(14.6%)at-0.2 V vs.reversible hydrogen electrode(RHE)toward NRR under ambient conditions.The superior catalytic performance of such non-precious metal catalysts would strongly promote the application of NRR process industrially.

Tuning the crystallite size of monoclinic ZrO_(2) to reveal critical roles of surface defects on m–ZrO_(2) catalyst for direct synthesis of isobutene from syngas

The effects of crystallite size on the physicochemical properties and surface defects of pure monoclinic ZrO_(2) catalysts for isobutene synthesis were studied.We prepared a series of monoclinic ZrO_(2) catalysts with different crystallite size by changing calcination temperature and evaluated their catalytic performance for isobutene synthesis from syngas.ZrO_(2) with small crystalline size showed higher CO conversion and isobutene selectivity,while samples with large crystalline size preferred to form dimethyl ether(DME)instead of hydrocarbons,much less to isobutene.Oxygen defects(ODefects)analyzed by X-ray photoelectron spectroscopy(XPS)provided evidence that more ODefectsoccupied on the surface of ZrO_(2) catalysts with smaller crystalline size.Electron paramagnetic resonance(EPR)and ultraviolet–visible diffuse reflectance(UV–vis DRS)confirmed the presence of high concentration of surface defects and Zr3+on mZrO_(2)-5.9 sample,respectively.In situ diffuse reflectance infrared Fourier transform spectroscopy(in situ DRIFTS)analysis indicated that the adsorption strength of formed formate species on catalyst reduced as the crystalline size decreased.These results suggested that surface defects were responsible for CO activation and further influenced the adsorption strength of surface species,and thus the products distribution changed.This study provides an in-depth insight for active sites regulation of ZrO_(2) catalyst in CO hydrogenation reaction.

Surface defect-rich ceria quantum dots anchored on sulfur-doped carbon nitride nanotubes with enhanced charge separation for solar hydrogen production

Designing defect-engineered semiconductor heterojunctions can effectively promote the charge carrier separation.Herein,novel ceria(CeO2) quantum dots(QDs) decorated sulfur-doped carbon nitride nanotubes(SCN NTs) were synthesized via a thermal polycondensation coupled in situ depositionprecipitation method without use of template or surfactant.The structure and morphology studies indicate that ultrafine CeO2 QDs are well distributed inside and outside of SCN NTs offering highly dispersed active sites and a large contact interface between two components.This leads to the promoted formation of rich Ce^(3+) ion and oxygen vacancies as confirmed by XPS.The photocatalytic performance can be facilely modulated by the content of CeO2 QDs introduced in SCN matrix while bare CeO2 does not show activity of hydrogen production.The optimal catalyst with 10% of CeO2 loading yields a hydrogen evolution rate of 2923.8 μmol h-1 g-1 under visible light,remarkably higher than that of bare SCN and their physical mixtures.Further studies reveal that the abundant surface defects and the created 0 D/1 D junctions play a critical role in improving the separation and transfer of charge carriers,leading to superior solar hydrogen production and good stability.

Laser ultrasonic technique for surface defects detection of 6061 aluminum alloy

In order to estimate and detect the surface defect depth of metals, the transmission method of laser ultrasonic surface waves is used in this work. The laser ultrasonic detection platform taking use of thermoelastic mechanism as acoustic signal excitation method and interference receiver as acoustic signal receiver method was built, by which B-scan images of detected specimens with surface defects were collected to establish the relationship between the transmission coefficient and depth of the surface defect. Experimental results show that the amplitude of transmitted acoustic signal is related to the depth of surface defect. At last, a fitted curve of transmission coefficient using measured experimental data is obtained to estimate depth of surface defect on the 6061 aluminum alloy. Furthermore, a surface defect depth of 0.3 mm is estimated by the fitting curve with an estimated error of 16%. Therefore, a experimental method using the transmission method by laser ultrasonic is presented in this paper.

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

Creation of surface defects on carbon nanofibers by steam treatment

A direct strategy for the creation of defects on carbon nanofibers (CNFs) has been developed by steam treatment.Nitrogen physisorption,XRD,Raman spectra,SEM and TEM analyses proved the existence of the new defects on CNFs.BET surface area of CNFs after steam treatment was enhanced from 20 to 378 m2/g.Pd catalysts supported on CNFs were also prepared by colloidal deposition method.The different activity of Pd/CNFs catalysts in the partial hydrogenation of phenylacetylene further demonstrated the diverse surfaces of CNFs could be formed by steam treatment.

Rail Surface Defect Detection Based on Improved UPerNet and Connected Component Analysis

To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.

Formation and control of the surface defect in hypo-peritectic steel during continuous casting:A review

Hypo-peritectic steels are widely used in various industrial fields because of their high strength,high toughness,high processability,high weldability,and low material cost.However,surface defects are liable to occur during continuous casting,which includes depression,longitudinal cracks,deep oscillation marks,and severe level fluctuation with slag entrapment.The high-efficiency production of hypo-peritectic steels by continuous casting is still a great challenge due to the limited understanding of the mechanism of peritectic solidification.This work reviews the definition and classification of hypo-peritectic steels and introduces the formation tendency of common surface defects related to peritectic solidification.New achievements in the mechanism of peritectic reaction and transformation have been listed.Finally,countermeasures to avoiding surface defects of hypo-peritectic steels duiring continuous casting are summarized.Enlightening certain points in the continuous casting of hypo-peritectic steels and the development of new techniques to overcome the present problems will be a great aid to researchers.

Research on Surface Defect Detection Method of E-TPU Midsole Based on Machine Vision

In the industrial production of expanded thermoplastic polyurethane (E-TPU) midsoles, the surface defects still rely on manual inspection at present, and the eligibility criteria are uneven. Therefore, this paper proposes an E-TPU midsole surface defect detection method based on machine vision to achieve automatic detection and defect classification. The proposed method is divided into three parts: image preprocessing, block defect detection, and linear defect detection. Image preprocessing uses RGB three channel self-inspection to identify scorch and color pollution. Block defect detection uses superpixel segmentation and background prior mining to determine holes, impurities, and dirt. Linear defect detection uses Gabor filter and Hough transform to detect indentation and convex marks. After image preprocessing, block defect detection and linear defect detection are simultaneously performed by parallel computing. The false positive rate (FPR) of the proposed method in this paper is 8.3%, the false negatives rate (FNR) of the hole is 4.7%, the FNR of indentation is 2.1%, and the running time does not exceed 1.6 s. The test results show that this method can quickly and accurately detect various defects in the E-TPU midsole.

Impact of Methanol Photomediated Surface Defects on Photocatalytic H2 Production Over Pt/TiO2

Co-catalysts play a critical role in enhancing the efficiency of inorganic semiconductor photocatalysts;however,synthetic approaches to tailoring cocatalyst properties are rarely the focus of research efforts.A photomediated route to control the dispersion and oxidation state of a platinum(Pt)cocatalyst through defect generation in the P25 titania photocatalyst substrate is reported.Titania photoirradiation in the presence of methanol induces longlived surface defects which subsequently promote the photodeposition of highly dispersed(2.2±0.8 nm)and heavily reduced Pt nanoparticles on exposure to H2 PtCl6.The optimal methanol concentration of 20 vol%produces the highest density of metallic Pt nanoparticles.Photocatalytic activity for water splitting and associated hydrogen(H2)production under UV irradiation mirrors the methanol concentration employed during the P25 photoirradiation pretreatment and resulting Pt loading resulting in a common mass-normalized H2 productivity of 3800±130 mmol gpt-1 h-1.Photomediated surface defects(arising in the presence of a methanol hole scavenger)provide electron traps that regulate subsequent photodeposition of a Pt co-catalyst over P25,offering a facile route to tune photocatalytic efficiency.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.