Image Strips Research Articles

A Method for Measuring Shaft Diameter Based on Light Stripe Image Enhancement.

When the workpiece surface exhibits strong reflectivity, it becomes challenging to obtain accurate key measurements using non-contact, visual measurement techniques due to poor image quality. In this paper, we propose a high-precision measurement method shaft diameter based on an enhanced quality stripe image. By capturing two stripe images with different exposure times, we leverage their different characteristics. The results extracted from the low-exposure image are used to perform grayscale correction on the high-exposure image, improving the distribution of stripe grayscale and resulting in more accurate extraction results for the center points. The incorporation of different measurement positions and angles further enhanced measurement precision and robustness. Additionally, ellipse fitting is employed to derive shaft diameter. This method was applied to the profiles of different cross-sections and angles within the same shaft segment. To reduce the shape error of the shaft measurement, the average of these measurements was taken as the estimate of the average diameter for the shaft segment. In the experiments, the average shaft diameters determined by averaging elliptical estimations were compared with shaft diameters obtained using a coordinate measuring machine (CMM) the maximum error and the minimum error were respectively 18 μm and 7 μm; the average error was 11 μm; and the root mean squared error of the multiple measurement results was 10.98 μm. The measurement accuracy achieved is six times higher than that obtained from the unprocessed stripe images.

Steel Strip Surface Defect Detection Method Based on Improved YOLOv5s.

Steel strip is an important raw material for the engineering, automotive, shipbuilding, and aerospace industries. However, during the production process, the surface of the steel strip is prone to cracks, pitting, and other defects that affect its appearance and performance. It is important to use machine vision technology to detect defects on the surface of a steel strip in order to improve its quality. To address the difficulties in classifying the fine-grained features of strip steel surface images and to improve the defect detection rate, we propose an improved YOLOv5s model called YOLOv5s-FPD (Fine Particle Detection). The SPPF-A (Spatial Pyramid Pooling Fast-Advance) module was constructed to adjust the spatial pyramid structure, and the ASFF (Adaptively Spatial Feature Fusion) and CARAFE (Content-Aware ReAssembly of FEatures) modules were introduced to improve the feature extraction and fusion capabilities of strip images. The CSBL (Convolutional Separable Bottleneck) module was also constructed, and the DCNv2 (Deformable ConvNets v2) module was introduced to improve the model's lightweight properties. The CBAM (Convolutional Block Attention Module) attention module is used to extract key and important information, further improving the model's feature extraction capability. Experimental results on the NEU_DET (NEU surface defect database) dataset show that YOLOv5s-FPD improves the mAP50 accuracy by 2.6% before data enhancement and 1.8% after SSIE (steel strip image enhancement) data enhancement, compared to the YOLOv5s prototype. It also improves the detection accuracy of all six defects in the dataset. Experimental results on the VOC2007 public dataset demonstrate that YOLOv5s-FPD improves the mAP50 accuracy by 4.6% before data enhancement, compared to the YOLOv5s prototype. Overall, these results confirm the validity and usefulness of the proposed model.

Nonconvex fractional order total variation based image denoising model under mixed stripe and Gaussian noise

In this paper, we propose a minimization-based image denoising model for the removal of mixed stripe and Gaussian noise. The objective function includes the prior information from both the stripe noise and image. Specifically, we adopted a unidirectional regularization term and a nonconvex group sparsity term for the stripe noise component, while we utilized a nonconvex fractional order total variation (FTV) regularization for the image component. The priors for stripes enable adequate extraction of periodic or non-periodic stripes from an image in the presence of high levels of Gaussian noise. Moreover, the nonconvex FTV facilitates image restoration with less staircase artifacts and well-preserved edges and textures. To solve the nonconvex problem, we employed an iteratively reweighted $ \ell_1 $ algorithm, and then the alternating direction method of multipliers was adopted for solving subproblems. This led to an efficient iterative algorithm, and its global convergence was proven. Numerical results show that the proposed model provides better denoising performance than existing models with respect to visual features and image quality evaluations.

Comparison of test strip, conductivity, and novel smartphone digital image colorimetry methods for field assessment of soil chloride and salinity.

Understanding the extent of salt-affected soils is important in agriculture for irrigation, crop selection and land management, and affordable smartphone-based techniques can broaden access to environmental analysis in resource-limited settings. A novel smartphone-based Digital Image Colorimetry (DImC) technique using colorimetric chloride test strips has been developed and compared to 3 other field techniques (direct soil conductivity (Csoil), soil extract solution conductivity (Cext), titration test strips (TS)) and 1 lab-based method (chloride analyser (CA)) for the assessment of soil salinity. A survey of soils from a coastal saltmarsh and neighbouring farmland was conducted in Southwest Scotland and a rapid sample preparation method suitable for in-field use was evaluated. Comparison of each method with the reference method (CA) using correlation plots, Bland-Altman plots, and concordance correlation coefficients, showed that the accuracy and precision of the methods decreased in the order Cext > DImC ≃ TS > Csoil. The novel DImC method had good accuracy, although the limit of detection (LOD = 20.30 mg L-1) and precision was poorer than Cext. The analysis of field-wet soils enabled a rapid estimate of soil Cl- in approximately 6 minutes and spatially interpolated maps of soil Cl- concentration, prepared using smartphone location data, identified areas of elevated soil salinity. Each field analysis method had specific benefits, and the low cost, portability and lack of specialist equipment required for the DImC method demonstrated suitability for use in agriculture, citizen science or conservation settings where resources may be limited. The example presented demonstrates how smartphones may be used to broaden access to environmental analysis techniques.

An imidazo[1,2-a]pyridine-functionalized xanthene fluorescent probe for naked-eye detection of Hg2+ and its application in cell imaging and test strips

A fluorescent probe was synthesized with high pH tolerance and was made into test strips for Hg2+ detection in water samples.

TryonCM2: Try-on-Enhanced Fashion Compatibility Modeling Framework.

Recently, fashion compatibility modeling, which can score the matching degree of several complementary fashion items, has gained increasing research attention. Previous studies have primarily learned the features of fashion items and utilize their interaction as the fashion compatibility. However, the try-on looking of an outfit help us to learn the fashion compatibility in a combined manner, where items are spatially distributed and partially covered by other items. Inspired by this, we design a try-on-enhanced fashion compatibility modeling framework, named TryonCM2, which incorporates the try-on appearance with the item interaction to enhance the fashion compatibility modeling. Specifically, we treat each outfit as a sequence of items and adopt the bidirectional long short-term memory (LSTM) network to capture the latent interaction of fashion items. Meanwhile, we synthesize a try-on template image to depict the try-on appearance of an outfit. And then, we regard the outfit as a sequence of multiple image stripes, i.e., local content, of the try-on template, and adopt the bidirectional LSTM network to capture the contextual structure in the try-on appearance. Ultimately, we combine the fashion compatibility lying in the item interaction and try-on appearance as the final compatibility of the outfit. Both the objective and subjective experiments on the existing FOTOS dataset demonstrate the superiority of our framework over the state-of-the-art methods.

BCNO quantum dots-based ratiometric fluorescence platform integrated with portable device: Hypoxanthine sensing for on-site assessment of meat freshness with deep learning

Deep learning algorithms have been applied to digital image colorimetry (DIC) to improve color measurement and analysis, enabling the identification and quantification of analytes. In this work, we developed a smartphone-assisted platform for the detection of hypoxanthine (Hx) based on a ratiometric fluorescent probe (RF-probe) and deep learning algorithm for the first time with advantages of high sensitivity, in-field and simple operation, and accurate detection. Through an enzymatic cascade reaction, xanthine oxidase (XOD) degraded Hx to uric acid (UA) and H2O2, o-phenylenediamine (OPD) was converted to 2,3-diaminophenoxazine (DAP) by catalytic oxidation of H2O2 and OPD under horseradish peroxidase (HRP). The presence of DAP led to fluorescence quenching of BCNO quantum dots (BCNO QDs) at 440 nm and fluorescence enhancement of DAP at 570 nm due to the internal filtration effect (IFE). With the addition of Hx, the fluorescence color changed from blue to yellow. A good linear relationship was between I570/I440 and the Hx concentration in the range of 3-100 µM with a detection limit of 50 nM. Fluorescent color images of solution and test strips were acquired and extracted with a smartphone of our designed miniature device, which could be used to establish DIC for the detection of Hx with the linear relationship ranging from 0-100 μM and 0-40 μM, respectively. Then YOLOv5-assisted deep learning was applied to recognize and deal with the images, which offered improved accuracy, faster inference speed, flexibility, and efficient training. Therefore, our simple DIC platform was established for the in-field detection of Hx in real meat samples which was significant for the analysis of meat freshness and provided broad application potential for the analysis of other targets in food safety.

ESTIMATION OF SENSOR OFFSETS FOR A UAV PLATFORM USING TIEPOINTS ONLY

Abstract. In a UAV system, physical sensor offsets occur between an observation sensor and a GPS/IMU sensor which represents the position of UAV. The difference of angle between the GPS/IMU sensor’s axis and the observation sensor’s axis is referred to as boresight angle. The difference in physical position between the two is referred to as lever-arm. It is important to obtain accurate offset values in order to utilize UAVs in rapid mapping manner. Due to the sensor offsets, misalignment error can be caused when generating a mosaic image. Offset values can be measured by using extensive ground control points. However, this is very costly and time-consuming. In this study, we describe an estimation method for sensor offsets using tie-points and re-weighted least square estimation method. The proposed method consists of 5 steps. Firstly, a frame images for the target area were classified into image strips based on the kappa value of initial EOPs (Exterior Orientation Parameters), after which tie points were extracted between adjacent images. Secondly, strip bundle adjustment was performed to update initial EOPs using images with the same flight direction. Thirdly, tie-points between adjacent strips were extracted. Fourthly, block bundle adjustment was performed in all images, using all extracted tie-points. Finally, a mosaic image is generated using the EOPs value to which the estimated sensor offset is applied. From this study, we confirmed that the sensor offset of the platform could be estimated only with the tie-points extracted between adjacent images. And we confirmed that misalignment was adjusted when generating mosaic image. We expect that our research makes UAV system to be operated more fluently.

Lateral Flow Immunoassay Based on Quantum-Dot Nanobeads for Detection of Chloramphenicol in Aquatic Products.

Quantum dot nanobeads (QBs) were used as signal source to develop competitive lateral flow immunoassay (LFIA) for the detection of chloramphenicol (CAP). The quantitative detection of CAP was achieved by calculating the total color difference (∆E) values of the test line (T line) using the images of test strips. QB-based LFIA (QBs-LFIA) allowed the effective dynamic linear detection of CAP in the range of 0.1-1.5 ng/mL. The limit of detection (LOD) was 3.0 ng/mL, which was 50 and 667 times lower than those achieved for two different brands of colloidal gold kits. The recoveries of CAP during real-sample detection were 82.82-104.91% at spiked levels of 0.1, 0.7, and 1.5 ng/mL. These results indicate that the developed QBs-LFIA facilitates the sensitive detection of CAP.

RSG-Net: A Recurrent Similarity Network With Ghost Convolution for Wheelset Laser Stripe Image Inpainting

RSG-Net: A Recurrent Similarity Network With Ghost Convolution for Wheelset Laser Stripe Image Inpainting

PMG—Pyramidal Multi-Granular Matching for Text-Based Person Re-Identification

Given a textual query, text-based person re-identification is supposed to search for the targeted pedestrian images from a large-scale visual database. Due to the inherent heterogeneity between different modalities, it is challenging to measure the cross-modal affinity between visual and textual data. Existing works typically employ single-granular methods to extract local features and align image regions with relevant words/phrases. Nevertheless, the limited robustness of single-granular methods cannot adapt to the imprecision and variances of visual and textual features, which are usually influenced by the background clutter, position transformation, posture diversity, and occlusion in surveillance videos, thereby leading to the deterioration of cross-modal matching accuracy. In this paper, we propose a Pyramidal Multi-Granular matching network (PMG) that incorporates a gradual transition process between the coarsest global information and the finest local information by a coarse-to-fine pyramidal method for multi-granular cross-modal features extraction and affinities learning. For each body part of a pedestrian, PMG is adequate in ensuring the integrity of local information while minimizing the surrounding interference signals at a certain scale and can adapt to capture discriminative signals of different body parts and achieve semantically alignment between image strips with relevant textual descriptions, thus suppressing the variances of feature extraction and improving the robustness of feature matching. Comprehensive experiments are conducted on the CUHK-PEDES and RSTPReid datasets to validate the effectiveness of the proposed method and results show that PMG outperforms state-of-the-art (SOTA) methods significantly and yields competitive accuracy of cross-modal retrieval.

Laser stripe center extraction method base on Hessian matrix improved by stripe width precise calculation

The line laser measurement technology based on triangulation is widely used in the field of complex surface topography measurement. The key technology of line laser measurement is laser stripe center extraction. For high reflective surfaces, due to the low surface roughness, some areas of the laser stripe image are overexposed, resulting in uneven stripe width distribution, asymmetric light intensity distribution of the stripe cross section, and difficulty in extracting the center of the stripe. In this paper, Laser stripe center extraction method based on Hessian matrix method improved by stripe width is proposed. Firstly, the laser stripe image is preprocessed, the influence of noise in the image is alleviated by image filtering and adaptive threshold segmentation, and the region of interest of the image is extracted to reduce the amount of subsequent calculation. The laser stripe width is calculated based on BPNN and divided into different width intervals. The Gaussian kernel is calculated according to the light stripe width and the Hessian matrix is constructed. The Hessian matrix method is used to roughly extract the center coordinates of the light stripe. Based on the continuity of the light stripe, the center coordinates of the light stripe are weighted and corrected to realize the smooth optimization of the center coordinates of the light stripe. The experimental results show that the proposed light stripe center extraction method is superior to the traditional method under different surface roughness and exposure time conditions, which can effectively improve the measurement accuracy of the sensor.

EggCountAI: a convolutional neural network-based software for counting of Aedes aegypti mosquito eggs

BackgroundMosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. In existing egg-counting tools, each image requires separate processing with adjustments to various parameters such as intensity threshold and egg area size. Furthermore, accuracy decreases significantly when dealing with clustered or overlapping eggs. To overcome these issues, we have developed EggCountAI, a Mask Region-based Convolutional Neural Network (RCNN)-based free automatic egg-counting tool for Aedes aegypti mosquitoes.MethodsThe study design involves developing EggCountAI for counting mosquito eggs and comparing its performance with two commonly employed tools—ICount and MECVision—using 10 microscopic and 10 macroscopic images of eggs laid by females on a paper strip. The results were validated through manual egg counting on the strips using ImageJ software. Two different models were trained on macroscopic and microscopic images to enhance egg detection accuracy, achieving mean average precision, mean average recall, and F1-scores of 0.92, 0.90, and 0.91 for the microscopic model, and 0.91, 0.90, and 0.90 for the macroscopic model, respectively. EggCountAI automatically counts eggs in a folder containing egg strip images, offering adaptable filtration for handling impurities of varying sizes.ResultsThe results obtained from EggCountAI highlight its remarkable performance, achieving overall accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI significantly outperformed ICount and MECVision, with ICount achieving 81.71% accuracy for micro images and 82.22% for macro images, while MECVision achieved 68.01% accuracy for micro images and 51.71% for macro images. EggCountAI also excelled in other statistical parameters, with mean absolute error of 1.90 eggs for micro, 74.30 eggs for macro, and a strong correlation and R-squared value (0.99) for both micro and macro. The superior performance of EggCountAI was most evident when handling overlapping or clustered eggs.ConclusionAccurate detection and counting of mosquito eggs enables the identification of preferred egg-laying sites and facilitates optimal placement of oviposition traps, enhancing targeted vector control efforts and disease transmission prevention. In future research, the tool holds the potential to extend its application to monitor mosquito feeding preferences.Graphical

Feature information extraction method for narrow gap U-type groove based on laser vision

The precise measurement of weld groove position and geometric dimensions serves as the foundation for predicting and planning welding strategies and process parameters. The steep sidewalls of narrow gap U-type grooves pose challenges for sensing and detection during the welding process. This paper presents a feature information extraction method for narrow gap U-type grooves based on laser vision technology. Firstly, the image acquisition system is calibrated using the incident plane of the laser beam. Subsequently, a digital image processing technique is employed to enhance the collected images, combining a 3D spatial domain enhancement algorithm with a laser stripe image refinement method based on Euclidean distance transformation. Finally, the transverse and vertical positions of the groove, as well as its geometric features such as width, depth, assembly gap, and misalignment, are computed and measured using feature point positioning. The proposed algorithm is experimentally verified for its accuracy, robustness, and real-time performance. The results indicate that the method effectively suppresses unevenness in laser stripe brightness and mitigates random noise in the captured images. The average measurement errors for transverse position, vertical position, width, depth, assembly gap, and misalignment of the groove are 0.0857 mm, 0.0987 mm, 0.1664 mm, 0.2216 mm, 0.0935 mm, and 0.0997 mm, respectively. Furthermore, the average image processing time per frame is 58 ms, meeting the requirements of pre-weld detection in arc welding.

Real-Time Processing and High-Quality Imaging of Navigation Strip Data Using SSS Based on AUVs

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Conv-TabNet: an efficient adaptive color correction network for smartphone-based urine component analysis.

The camera function of a smartphone can be used to quantitatively detect urine parameters anytime, anywhere. However, the color captured by different cameras in different environments is different. A method for color correction is proposed for a urine test strip image collected using a smartphone. In this method, the color correction model is based on the color information of the urine test strip, as well as the ambient light and camera parameters. Conv-TabNet, which can focus on each feature parameter, was designed to correct the color of the color blocks of the urine test strip. The color correction experiment was carried out in eight light sources on four mobile phones. The experimental results show that the mean absolute error of the new method is as low as 2.8±1.8, and the CIEDE2000 color difference is 1.5±1.5. The corrected color is almost consistent with the standard color by visual evaluation. This method can provide a technology for the quantitative detection of urine test strips anytime and anywhere.

URS: A Light-Weight Segmentation Model for Train Wheelset Monitoring

To detect the wheelset deformation and wear, an intuitive method is to first collect wheelset multi-line laser stripe images by monitors, and then extract the centerlines to construct 3D contours that are transferred to the cloud data center by 6G communication. The images, however, contain flairs and fractures due to the influence of environmental interference and reflected light on the smooth surfaces. The image defects affect the accurate extraction of stripe centerlines. To segment the defects and inpaint them, we propose a new lightweight U-shaped segmentation model URS. A target-shaped receptive field is designed to efficiently extract the details of the local, the ring-shaped, and the cross-shaped context around the local, which facilitates segmenting various defects. A scale-select sub-module is designed to adjust the weights of features from different receptive fields. To train the model, a multi-line laser image defect segmentation dataset MLIDSD is constructed. Experiments demonstrate that the performance of our model surpasses twelve SOTA models explicitly and can achieve a balance between the accuracy and the lightweight requirement.

A Novel Electromagnetic Noise and Image Stripe Noise Suppression Method Between SMA-OIS Actuator and CMOS Image Sensor

In order to solve the electromagnetic compatibility (EMC) problem between shape memory alloy (SMA) optical image stabilization (OIS) actuator and CMOS image sensor (CIS), a novel electromagnetic noise and image stripe noise (ISN) suppression method is proposed in this paper. The electromagnetic interference mechanism of SMA-OIS actuator is analyzed by electromagnetic field simulation and magnetic dipole mathematical model. The CIS noise mechanism based on the readout circuit model and image signal processing (ISP) pipeline is analyzed and the ISN evaluation method of CIS is proposed. The drive frequency and ISN model is established by frequency sweep experiment through the joint experimental platform of SMA-OIS actuator and CIS. Furthermore, the random frequency modulation PWM method based on the spread spectrum principle is proposed to suppress the electromagnetic noise and ISN. The experimental results show that the state-of-the-art noise suppression method proposed in this paper can not only eliminate ISN, but also have good technical adaptability in practical applications.

A Curvelet-Transform-Based Image Fusion Method Incorporating Side-Scan Sonar Image Features

Current methods of fusing side-scan sonar images fail to tackle the issues of shadow removal, preservation of information from adjacent strip images, and maintenance of image clarity and contrast. To address these deficiencies, a novel curvelet-transform-based approach that integrates the complementary attribute of details from side-scan sonar strip images is proposed. By capitalizing on the multiple scales and orientations of the curvelet transform and its intricate hierarchical nature, myriad fusion rules were applied at the corresponding frequency levels, enabling a more-tailored image fusion technique for side-scan sonar imagery. The experimental results validated the effectiveness of this method in preserving valuable information from side-scan sonar images, reducing the presence of shadows and ensuring both clarity and contrast in the fused images. By meeting the aforementioned challenges encountered in existing methodologies, this approach demonstrated great practical significance.

Rational development of a peptide-based probe for fluorescence and colorimetric dual-mode detection of Cu2+ and S2− ions: Real application in cell imaging and test strips

A new dual-mode probe FAM-SSH with fluorescence and colorimetric properties was developed by solid-phase peptide synthesis, comprising 5-carboxy fluorescein (5-FAM) as a fluorophore, and tripeptide (Ser-Ser-His) as a recognition group. FAM-SSH not only displayed highly selective detection of Cu2+ based on fluorescence quenching mode, but also achieved colorimetric recognition of Cu2+ in solution, wherein a color change was observable to the naked eye. Additionally, the FAM-SSH-Cu2+ ensemble was highly selective for S2− over a wide pH range (7.0–12.0), characterized by a fluorescence enhanced response and colorimetric recognition, which was caused by the release of FAM-SSH and the precipitation of CuS. Moreover, the limit of detection (LOD) values for Cu2+ and S2− were 55.5 nM and 31.1 nM, respectively. Results of sample analyses and cell imaging experiments indicated that FAM-SSH has exciting field practicability and good cell permeability, and would be further useful for detection and imaging in environmental systems and living cells. Finally, test strips were produced by immersing them in FAM-SSH solution, thereby creating a method for portable visual detection. More importantly, a smartphone-assisted visual sensing platform was also developed for semi-quantitative Cu2+ and S2− detection with LOD values of 0.48 μM and 1.22 μM, respectively.