Scratch Detection Research Articles

Multi-Responsive Polymer Nanoparticles: A Versatile Platform for Double-Security Anticounterfeiting and Smart Food Packaging.

Potential applications of colloidal polymer nanoparticles in the preparation of smart inks are investigated by physical incorporation of the oxazolidine molecules. Precise adjusting the polymer chain flexibility and polarity is achieved by controlling the ratio of methyl methacrylate and butyl acrylate monomers in the polymerization reaction. In addition, nanofibrous indicators of acid-base vapors are prepared from the latex nanoparticles. This can be beneficial for creating materials that sense and respond to environmental changes, such as humidity or moisture and acidity. Thermochromic inks are prepared by microencapsulating crystal violet lactone dye (CVL) in polymer matrices to prevent their release into the aqueous media. Combining two distinct systems with varying triggers, such as light and temperature, provides an effective strategy for double-encryption anticounterfeiting and crack and scratch detection and indication applications. Preparing labels impregnated with double-responsive inks, a novel approach is developed for food spoilage detection and preservation indication. Labels are manufactured using polymer nanoparticles, which contain photoluminescent oxazolidine molecules, as well as a trinary mixture of CVL within core-shell latex particles as the thermochromic dye. The combination of these two responsive elements transforms traditional packaging into a dynamic and interactive sentinel for the food it holds.

Enhanced Scratch Detection for Textured Materials Based on Optimized Photometric Stereo Vision and Fast Fourier Transform–Gabor Filtering

In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. We designed and optimized a novel hemispherical image acquisition device that allows for selective lighting angles. This device integrates images captured under multiple light sources to obtain richer surface gradient information for textured materials, overcoming issues caused by high reflections or dark shadows under a single light source angle. At the same time, for the textured material, scratches and a textured background are difficult to distinguish; therefore, we introduced a Gabor filter-based convolution kernel, leveraging the fast Fourier transform (FFT), to perform convolution operations and spatial domain phase subtraction. This process effectively enhances the defect information while suppressing the textured background. The effectiveness and superiority of the proposed method were validated through material applicability experiments and comparative method evaluations using a variety of textured material samples. The results demonstrated a stable scratch capture success rate of 100% and a recognition detection success rate of 98.43% ± 1.0%.

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.

Research on Bearing Surface Scratch Detection Based on Improved YOLOV5.

Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings.

Brake disc positioning and defect detection method based on improved Canny operator

AbstractFirstly, the Canny operator combined with circle fitting is used to position the working surface. Secondly, the median filter and the Laplacian operator are selected for preprocessing. Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection. A bidirectional connection is introduced to optimize the edge detection process of the Canny operator, and a parallel algorithm is introduced to shorten the detection time. Finally, the standard Hough transform needs to traverse the entire parameter space, and the calculation is complicated, and the probability Hough transform is selected. The experimental results show that compared with the traditional Canny algorithm, the precision, recall, and F1‐score of the improved Canny algorithm are increased by 3.2%, 13.2%, and 6.0%, respectively. After adding parallel processing, the average detection time was reduced by 45 ms. Finally, the accuracy of scratches extracted using this algorithm reached 97.96%, the leak rate, and mistake rate of this study are only 3.33% and 2.00%, which provides a more accurate and efficient theoretical support for the research on brake disc scratch detection.

Scratch detection of archival films: modeling and trajectory calculation

Scratches are a common continuous structural type of damage in archival films. Scratch pixel level segmentation algorithms still use general image segmentation, and with poor the detection. Based on the characteristics of scratches, a scratch model based on a Gaussian probability density function is proposed, a local adaptive one-dimensional Gaussian weighted segmentation algorithm is designed, and an invalid region filtering mechanism is described. Based on experimental results, the local one-dimensional Gaussian weighted segmentation algorithm proposed in this paper can preserve the integrity and continuity of scratches, and effectively suppresses edge defects; the invalid region filtering mechanism can effectively reduce the false detection of scratches. Adding a scratch trajectory can effectively reduce the chances of missing the detection of scratches, and the stability of the traditional algorithm using Kalman filters to predict the scratch trajectory is poor. According to the similarity of multiple scratch trajectories, a calculation method of trajectories based on U-net is described. Experimental results show that this method can calculate the scratch trajectory stably and effectively.

Colloidal Polymer Nanoparticles as Smart Inks for Authentication and Indication of Latent Fingerprints and Scratch.

An environmentally friendly smart ink was developed by incorporating fluorescein into functionalized poly(methyl methacrylate) (PMMA) nanoparticles synthesized using an emulsifier-free emulsion copolymerization approach. The functional comonomers of 2-(dimethylamino)ethyl methacrylate (DMAEMA), acrylamide, hydroxyethyl methacrylate, and glycidyl methacrylate in 10 wt % with respect to methyl methacrylate were used to obtain the functionalized colloidal PMMA nanoparticles. Functional groups of the latex nanoparticles were characterized by Fourier-transform infrared spectroscopy. Field emission scanning electron microscopy results showed that all of the latex nanoparticles have nearly spherical morphologies with variations in size and surface smoothness due to the presence of different comonomers. Ultraviolet-visible and fluorescence spectra indicated that the fluorescein-doped latex nanoparticles containing the DMAEMA comonomer had the highest absorbance and fluorescence intensity. In the alkaline media, fluorescein turns to a dianion, showing a red shift and increased absorbance in the UV-vis spectroscopy. In addition, the electron inductive characteristics of the tertiary amine groups result in enhancing the conjugation of fluorescein molecules and increasing the fluorescence intensities. Therefore, the colloidal nanoparticles with amine functional groups were used in the formulation of a smart ink with applications in securing documents and fingerprints, encrypting banknotes and money, detecting latent fingerprints, crafting anticounterfeiting paper, and eventually providing optical detection and indication of surface scratches.

LIPUS-induced neurogenesis:A potential therapeutic strategy for cognitive dysfunction in traumatic brain injury

Traumatic brain injury (TBI) precipitates cellular membrane degeneration, phospholipid degradation, neuronal demise, impaired brain electrical activity, and compromised neuroplasticity, ultimately leading to acute and chronic brain dysfunction. Low-intensity pulsed ultrasound (LIPUS) is an emerging brain therapy with the characteristics of non-invasive, high spatial resolution, and high stimulation depth. Herein, we established a controlled cortical impact model to investigate the potential reparative mechanisms of LIPUS in TBI, employing a multi-faceted research methodology encompassing behavioral assessments, immunofluorescence, neuroelectrophysiology, scratch detection of primary cortical neurons, metabolomics and transcriptomics. Our findings demonstrate that LIPUS promotes hippocampal neurogenesis following brain injury, accomplished through the elevation of phosphatidylcholine levels in the hippocampus of TBI mice. Consequently, LIPUS enhances neural electrical activity and augments neural plasticity within the CA1 subregion of the hippocampus, effectively restoring neuronal function and cognitive capabilities in TBI mice. These findings shed light on the promising role of LIPUS in TBI brain rehabilitation, offering new perspectives and theoretical foundations for future studies in this domain.

A multimodal sensing ring for quantification of scratch intensity

BackgroundAn objective measurement of chronic itch is necessary for improvements in patient care for numerous medical conditions. While wearables have shown promise for scratch detection, they are currently unable to estimate scratch intensity, preventing a comprehensive understanding of the effect of itch on an individual.MethodsIn this work, we present a framework for the estimation of scratch intensity in addition to the detection of scratch. This is accomplished with a multimodal ring device, consisting of an accelerometer and a contact microphone, a pressure-sensitive tablet for capturing ground truth intensity values, and machine learning algorithms for regression of scratch intensity on a 0–600 milliwatts (mW) power scale that can be mapped to a 0–10 continuous scale.ResultsWe evaluate the performance of our algorithms on 20 individuals using leave one subject out cross-validation and using data from 14 additional participants, we show that our algorithms achieve clinically-relevant discrimination of scratching intensity levels. By doing so, our device enables the quantification of the substantial variations in the interpretation of the 0–10 scale frequently utilized in patient self-reported clinical assessments.ConclusionsThis work demonstrates that a finger-worn device can provide multidimensional, objective, real-time measures for the action of scratching.

Surface weak scratch detection for optical elements based on a multimodal imaging systemand a deep encoder-decoder network.

The detection of surface weak scratches is an intractable but vital task in optics-centered industries. However, the intrinsic characteristics of weak scratches, such as a narrow width, long span, and shallow depth, make it extremely difficult to effectively detect these scratches. In this paper, we tackle this issue from two perspectives. First, a multimodal microscopic imaging system is created by combining discrete multispectral illumination with linear polarization. Imaging experiments demonstrated that this system could highlight more scratch details, improve image clarity, and alleviate the image blur problem induced by wide spectrum scattered lights. Second, a scratch-oriented U-shaped deep encoder-decoder network equipped with optimized residual encoding modules, serial-parallel multiscale fusion modules, and triple-convolution decoding modules is proposed to segment the weak scratches from a raw image. The detection experiments demonstrate that our model can accurately segment the weak scratches on optical surfaces and achieve better detection performance using significantly fewer parameters compared to similar deep learning models. Meanwhile, experiments on the building crack dataset prove the excellent generalization capability.

The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5

In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.

285 Performance metrics of a soft wearable acousto-mechanic sensor for measuring itch in children and adults with atopic dermatitis

Abstract Pruritus is a prominent symptom in many systemic (e.g. renal failure, cholestasis) and dermatologic (e.g. urticaria, xerosis) conditions. Atopic dermatitis (AD) is characterized by the itch-scratch cycle, whereby the reflexive scratching leads to greater skin inflammation and worsens pruritus. Itch in AD has also been associated with certain stages of the circadian rhythm. Therefore, an objective method of measuring nocturnal scratching behaviour as a proxy for itch symptom severity would prove useful and insightful in the management of a patient’s AD. The purpose of this study is to validate the performance of a novel wearable sensor, the advanced acousto-mechanic (ADAM) device, in regard to the detection of nocturnal scratching behaviour in paediatric and adult populations with AD. The sensor is able to detect both the accelerometer data (movement in space) and acoustic data generated by finger and wrist movements in scratching. Performance metrics used to evaluate the sensor include sensitivity, specificity, positive predictive value (PPV) and F1 score (combination of precision and recall). The sensor’s performance was compared to video recordings, the gold standard for objectively measuring scratch. A total of 60 healthy adult subjects (22 males and 38 females) were recruited and asked to perform a series of scratching behaviours and non-scratching behaviours while wearing the ADAM sensor on their dorsal hand using a one-use adhesive in a controlled environment. The data were used to create a machine-learning algorithm for scratch detection and validation in both a paediatric and adult AD cohort. Individuals with mild-to-severe AD were recruited to monitor their nocturnal scratching behaviour in the home environment. An infrared camera was provided for each subject to record the patient’s scratching and compared it to data collected by the ADAM sensor worn on the subject’s dominant dorsal hand. Video recordings were graded for scratching activity by at least two graders; scratch was defined as lasting at least 4.5 s. The scratch algorithm’s classification of scratch and non-scratch was then compared to the gold standard video recording and generated performance metrics. The initial scratch algorithm yielded sensitivity of 88%, specificity of 88% and F1 score of 90% when cross-validated with data from 10 healthy adult subjects. When cross-validated with data from all 60 healthy adult subjects, the algorithm yielded a sensitivity of 92%, specificity of 98% and F1 score of 95%. A total of 11 paediatric AD subjects (1 : 3 male-to-female ratio, ages 10.5 ± 9.1) and 46 nights of data were collected in the home setting, yielding a total of 378.4 h of video. The scratch algorithm was able to detect scratching behaviour with sensitivity of 84%, specificity of 99% and F1 score of 83% in the paediatric cohort. In the adult cohort, a total of 11 adults with atopic dermatitis (two males and nine females, ages 29 ± 13) and 73 nights of data were collected, resulting in a total of 457.7 h of video. When the data was applied to the scratch algorithm, the algorithm yielded a sensitivity of 93%, specificity of 100% and F1 score of 91%. The use of a flexible wearable sensor on the dorsal hand for detection of nocturnal scratching behaviour in both paediatric and adult subjects with atopic dermatitis can provide valuable information regarding the degree of scratching and severity of pruritus the wearer is experiencing when compared to video recording. Its ability to serve as an accurate objective measure of itch may be helpful in drug development and can help serve as a tool to guide the clinical management of symptoms.

Automatic detection of scratches on chicken carcass in slaughter factory using image processing and machine learning

Automatic detection of scratches on chicken carcass in slaughter factory using image processing and machine learning

Photoluminescent Janus oxazolidine nanoparticles for development of organic light-emitting diodes, anticounterfeiting, information encryption, and optical detection of scratch.

Photoluminescent polymer nanoparticles based on organic compounds are a new category of advanced materials with potential applications in various fields such as chemosensors, optical devices, drug-delivery systems, anticounterfeiting inks and bioimaging. Herein, amide functionalized copolymer nanoparticles with a particle size in the range of 42-139nm were synthesized by emulsion copolymerization of methylmathacrylate (MMA) and methacrylamide (MAAm) with different concentrations in the range of 0-20wt%. Afterward, the photoluminescent copolymer nanoparticles (PLCNPs) were prepared by physical modification of amide functionalized copolymer nanoparticles with 5wt% of oxazolidine derivative (OXOH). Investigation of optical properties include of photoluminescence and UV-vis spectra displayed significant dependency of emission and absorbance bands to the local polarity or the concentration of the amide groups on the surface of the nanoparticles. Study of Commission Internationale de l'Eclairage (CIE) colorimetric diagrams for PLCNPs samples confirmed the results of photoluminescence and UV-vis spectra, which a logical shift in color of the photoluminescence emission was observed by an increase in the concentration of the amide groups. The PLCNPs samples have remarkable stability (in physical and optical properties) when applied on polar substrates such as polymer sheet and cellulose paper. Therefore, the PLCNPs samples were used for development of eco-friendly water based anticounterfeiting inks, photoluminescence organic light emitting diodes (OLEDs), and also the photodetection of scratches in a fast and facile manner. The printed security tags and hand-written phrases on cellulosic papers have orange fluorescent emission with maximum intensity because nanoscale size of particles, which reduced the light scattering and increased the accessible concentration of OXOH for light absorption. A coating of poly(vinyl alcohol) (PVA)/PLCNPs solution onto a blue LED (365nm) was led to a change in the color of emission from blue to purple, because of the excitation of OXOH molecules at 365nm with the subsequent emission of purple light. The photodetection of created scratch on polycarbonate sheet was carried out by spray of PLCNPs solution on sheet and then illumination with UV irradiation (365nm). Developed water based PLCNPs samples have interesting optical properties, high stability on different surfaces and nanoscale particle size which are applicable in multiple applications such as eco-friendly photoluminescent inks for anticounterfeiting technologies, photoluminescence OLEDs, and also optical detector for monitoring of scratches on different substrates.

Design of dispersive objective of chromatic confocal for scratch detection

Design of dispersive objective of chromatic confocal for scratch detection

Mobile phone screen surface scratch detection based on optimized YOLOv5 model (OYm)

AbstractTo improve phone screen surface detection efficiency, an optimized YOLOv5s model (OYm) based on GhostNet(YOLOv5GHOSTs) and BottleneckCSP is proposed. For a given target sample, OYm could effectively reduce the computation of GFLOPS and detection time by optimizing the network structure. The detection results show that the mean average precision_0.5 (mAP_0.5) exceeds 95%, and the average detection rate is 16 ms. Compared with the traditional YOLOv5s model, the loss of average accuracy is ensured to be controlled within 3%, the detection frame rate of OYm is risen by 56.25%, and GFLOPS is decreased by 64.2%. The principle of OYm is explained in detail, and the proposed model is then experimentally validated.

A new lightweight deep neural network for surface scratch detection.

This paper aims to develop a lightweight convolutional neural network, WearNet, to realise automatic scratch detection for components in contact sliding such as those in metal forming. To this end, a large surface scratch dataset obtained from cylinder-on-flat sliding tests was used to train the WearNet with appropriate training parameters such as learning rate, gradient algorithm and mini-batch size. A comprehensive investigation on the network response and decision mechanism was also conducted to show the capability of the developed WearNet. It was found that compared with the existing networks, WearNet can realise an excellent classification accuracy of 94.16% with a much smaller model size and faster detection speed. Besides, WearNet outperformed other state-of-the-art networks when a public image database was used for network evaluation. The application of WearNet in an embedded system further demonstrated such advantages in the detection of surface scratches in sheet metal forming processes.

Visual Scratch Defect Detection System of Aluminum Flat Tube Based on Cubic Bezier Curve Fitting Using Linear Scan Camera

This paper presents a scratch detection system based on a cubic Bezier curve fitting using a linear scan camera. The objective was to detect the scratch defects of an aluminum flat tube stably in real-time under complex uncertain background noise. To that end, according to the features of the input image of the linear scan camera and the scratch defects, the proposed method first segmented the input image to ten equal sections in a longitudinal direction, and for every section, OTSU thresholding and morphological filtering were used to reduce the background noise. After the image preprocessing, every section image was projected along a vertical direction to form a vertical histogram. After that, for each point of every vertical histogram, a novel curve fitting method based on the Monte Carlo method was employed to calculate the best fitted Bezier curve. All the curvatures of the middle point of the best fitted Bezier curves then formed a curvature curve, and the scratches were located by finding the peaks of the curvature curve. Next, the result of the ten sections were fused to find the final positions of the scratches. The experimental results based on the linear scan camera that captured the image of flat tubes on a moving speed of 2m/s showed that the proposed method can detect the scratch defects under complex background noise with a high success rate in real-time.

Study on steel plate scratch detection based on improved MSR and phase consistency

In the casting process of the steel plate, due to the influence of rolling equipment and technology, the defects such as cracks and scratches appear on the surface of steel plate, which affect the performance of steel plate and even cause production accidents. In this paper, an automatic detection method for steel plate scratch is proposed. Firstly, the steel plate image is decomposed by channel and the enhanced image is obtained by the improved MSR (Multi-Scale Retinex) enhancement algorithm. Then, the phase consistency is detected after the Log Gabor wavelet transform and the scratch areas are obtained by the threshold segmentation and intersection of three channels. Finally, the scratch position is identified and the scratch characteristics such as width and length can be calculated. The results show that the minimum error of the characteristics measurement is only 2.28% in the experimental environment and 4.15% in the field environment, and the mean running time is 0.2826 s in the experimental environment and 0.3193 s in the field environment. It verifies that the proposed method is effective and practical.

Detection method for surface scratches of composite automotive components with high reflection and complicated background color

Abstract Current testing status have the problems that the surface background color pattern of high reflective carbon fiber auto parts is complex, the shape of scratch defects is irregular, and the shallow and micro scratches are not easy to be detected. Aiming at the problems, a scratch detection method combining innovative morphological processing and optimized Canny edge detection algorithm is proposed. The image acquired by an innovative image acquisition platform. After gray processing and open operation denoising, the self-defined oval kernel secondary expansion is introduced in the morphological processing part. When threshold segmentation and minimum connected domain screening are done, the optimized Canny edge detection algorithm is used. Through the non-maximum suppression of gradient amplitude, a wide range of dual-threshold parameters are selected to detect the edge of the target image. The results show that the detection rate of the proposed method is 18.57% higher than that of the traditional way, and the detection rate is up to 95.71%. At the same time, the proposed method can reflect the scratch morphology more intuitively.