摘要:A phytoplankton bright-field fluorescence microscopic simultaneous imaging system was designed to solve the problems of the conventional phytoplankton species identification microscopy method, such as the difficulty of distinguishing between similar morphological algal cells, interference of impurities of the same particle size, and difficulty in locating and segmenting the algal cells. The proposed system uses a high-resolution microscopic imaging optical path structure, combined transmission-fall illumination method, and simultaneous bright-field fluorescence dual-optical imaging method with a spectral optical path to capture bright-field morphological and fluorescence images of phytoplankton with pigment information. The system was tested for the simultaneous acquisition of bright-field and fluorescence images of different algal species, including chlorophyta, cyanophyta, and bacillariophyta. The experimental results indicated that the imaging resolution of the system was 1.5 μm, imaging period was 707 ms, texture of algal cells in the images captured by the system was clear, and cell edges were obvious. The system using the method of combining the bright-field images with fluorescent images can effectively locate the algal cells, reduce the influence of impurities in the samples on the procedure of algae identification, and increase the amount of information obtained from the algal microscopic images. This study provides a methodological reference for related research based on phytoplankton microscopic images.
摘要:To achieve high-quality integration of large-aperture lens groups, a transmission wavefront detection system that can achieve micron-level accuracy detection on a meter-scale span is urgently needed. In this study, to solve the problem of large-aperture transmission wavefront quality detection, the relative tilt of components and the change in the system wavefront introduced by the support structure were obtained by combining non-narrowband interference with fringe tracking. First, according to the optical fiber interconnection architecture, a sub-aperture time-sharing multiplexing measurement system was designed. Second, the mapping relationship between the slope measurement and the final system wavefront was established, and the effects of the slope reconstruction process on the wavefront at different spatial frequencies were analyzed. Finally, a desktop experimental system was used to validate the detection principle. At the test wavelength of 1550 nm, the interference-sensing signal-to-noise ratio was >15 dB, the measurement range was better than 5 μm, and the detection accuracy was higher than 0.5 μm. Using the proposed method, the large-aperture lens transmission wavefront can be detected over a wide range, with high robustness and high accuracy; this is of considerable significance for the construction of large-aperture large-field of view telescopes.
摘要:For precisely measuring the diameters of ruby balls in a non-contact manner, a compound secondary edge detection method is proposed. First, the image of the ruby ball is preprocessed, and the adaptive threshold Canny edge detection algorithm is used to detect the edge of the ruby ball image. Second, an image fusion algorithm based on the pixel weighted average is used to fuse the binary image of the ruby ball and the edge detection image. Then, the edge of the fused image is extracted, the cubic spline interpolation method is used to interpolate the edge image, the subpixel coordinates of the image edge are obtained via curve fitting, and circle fitting is performed according to the coordinates. Thereafter, the detection results are obtained through calibration. Compound secondary edge detection is performed for precisely measuring the diameter of a ruby ball. The experimental results indicate that the measurement accuracy for a 6-mm ruby ball diameter can reach 2 μm, and the positioning accuracy is less than 0.1 pixels, thus satisfying the measurement requirements of enterprises and providing better technical support for industrial automatic detection.
摘要:To solve the transmittance measurement problem of large-aperture infrared optical systems in complete machines, a single-channel infrared transmittance measurement method based on grayscale images is proposed, and a transmittance measurement device is established. First, a blackbody and a target are placed at the focal plane of the infrared optical system under measurement according to the specified position relationship, and an infrared thermal imager is placed at the entrance port of the infrared optical system to capture the radiation image. Then, the infrared thermal imager is placed in front of the blackbody to capture the radiation image of the blackbody. Finally, the mean value of the radiation image and the mean value of the blackbody radiation image are calculated; the ratio of the two values is the transmittance of the measured system. Upon measuring the infrared transmittance of a large-aperture infrared optical system by using the proposed method, the results indicate that the method can accurately measure the transmittance of large-aperture infrared optical systems. The absolute error between the measured value and the design value is 0.85%. Analyzing the uncertainty of the test results reveals that the transmittance measurement accuracy of the proposed method is approximately 1.04%. This method entails a simple measurement process and is effective for evaluating the transmittance of large-aperture infrared optical systems in complete machines.
摘要:An on-line six-degree-of-freedom (6-DOF) error measurement method and compensation model for XY stages were devised for improving the volumetric positional accuracy of the functional point. The positioning errors in the X- and Y-directions of the stage and the straightness error in the Z-direction were measured via laser interferometry, and the three angular motion errors around the X-, Y-, and Z-axes were measured using laser autocollimators. The volumetric positional errors of the functional point caused by the 6-DOF errors of the stage was analyzed, and an error compensation model based on the Abbe and Bryan principle was developed. An in-situ and on-line 6-DOF error measurement system based on the measurement method was developed and applied to a micro-nano coordinate measuring machine, which had high precision and a compact design. The capability of the system and the effectiveness of the model were experimentally verified, and the measurement uncertainty of the system was evaluated. SIOS laser interferometers were used as a reference. Experimental results indicated that the maximum positional errors at the reference functional point in the X-, Y-, and Z-directions were reduced from 1.7, 3.4, and 3 μm to 65, 81, and 109 nm, respectively, after error compensation, and the expanded uncertainty was 90, 98, and 158 nm (k=2), respectively. The proposed method and system can be used to increase the accuracy of XY stages.
摘要:Highly effective and adaptable fabrication of high-quality micro-hole arrays for safety filters remains challenging. Therefore, in this study, a combination of laser and electrochemical machining technology was developed for preparing micro-hole arrays for safety filters. An experimental processing system for laser–electrochemical combined machining was devised, the effects of process parameters on the processing quality were investigated, and a safety filter sample was prepared. The results indicated that in the laser processing, the laser cutting path significantly affects the outer shape of the through-hole, and the outer roundness of the through-hole can be significantly improved using the optimized concentric circle path (outer diameter: 270 μm, inner diameter: 230 μm, and spacing distance: 5 μm). A higher laser power and lower cutting speed correspond to a larger through-hole diameter and heat-affected zone. NaNO3-ethylene glycol solution as polishing electrolyte can form a diffusion layer on the machining surface for electrolytic polishing and remove the spatters and burrs generated by laser processing. Using the optimized process parameters, 253 micro-hole structures with a diameter of 0.31 mm were machined, and the variance of the inlet and outlet diameters was only 2.02 and 1.71, respectively, indicating a high dimensional consistency. The laser-electrochemical combined process has considerable potential for the fabrication of high-quality micro-hole arrays on thin-walled workpieces.
摘要:As regards microfluidic chips, the advancement of self-driven microfluidics is hindered by the limitations of microchannel and nanochannel fabrication techniques. Therefore, the fabrication of microchannels and nanochannels by using a diamond cutter wheel featuring serrated microtips to roll the surfaces of hard and brittle chip materials was proposed in this study. The mechanism of microchannel and nanochannel formation was analyzed through experimental studies, and the mechanisms of the process parameters and material properties, as well as the self-driven micro-rheological properties, were investigated. The results indicate that at a certain feed depth and the barometric pressure, stress concentration occurs on the material contact surface at the microtip of the cutter wheel. Once the crack penetration value between the indentations is reached, nanochannels are formed on the material surface at a speed significantly higher than the cutter wheel rolling speed, and microchannels are formed when the strength limit of the material is exceeded. The aspect ratio increases with the maximum stress. The maximum-stress ranges for nanochannel formation in 4H-SiC, sapphire, and optical glass are 266-750, 256-600, and 74-150 MPa respectively, with optical glass exhibiting nanochannels with aspect ratios as high as 1.1 and surface roughness values as low as 1 nm. Low-hardness materials can produce nanochannels with higher aspect ratios, while high-fracture toughness materials exhibit the highest surface quality. In addition, the self-driven microfluids in nanochannels can achieve flow velocities as high as 0.055 mm/s and doses as low as 0.001 μm3/s.
关键词:microchannel and nanochannel;diamond cutter wheel;4H-SiC;sapphire;optical glass
摘要:To address the low measurement accuracy resulting from the inability to detect ideal corners of an object, a binocular-vision-based measurement method incorporating the one-dimensional probabilistic Hough transform and local Zernike moment is proposed herein. First, the one-dimensional probabilistic Hough transform is used for line detection of the outer contour. Next, sub-pixel extraction is performed using the Zernike moment method in the region of interest (ROI) established according to the line detection, and sub-pixel points are screened in the intersection region of the ROI. Then, before matching the key points, sub-pixel edge lines are fitted using the orthogonal total least squares method. Finally, the three-dimensional spatial information of a continuous casting slab model is obtained via the triangulation principle, and the measurement is completed. Here, the continuous casting slab model is considered as the measurement object. Experimental results indicate that the minimum relative error of the proposed algorithm is 0.340 1%, which satisfies the measurement requirement. The average relative error in the length is 0.3945%, which is 80.01% and 74.63% smaller than those of the traditional SIFT and ORB algorithms, respectively. Compared with another method based on edge fitting, the measurement error and time consumption of the proposed algorithm are reduced by 34.11% and 39.07%, respectively, confirming its measurement accuracy and efficiency.
摘要:Herein, to realize high-precision crack and break defect detection in solar cells under electroluminescent (EL) conditions, the multi-scale You Only Look Once version 5(YOLOv5) model is used for solar-cell defect detection under real industrial conditions. First, an improved feature-extraction network combining deformable convolution version 2 (DCNv2) and coordinate attention (CA) is proposed to widen the receptive field of small target defects and enhance the extraction of small-scale defect features. Second, an improved path aggregation network (PANet), called CA-PANet, is proposed for integrating the CA and cross-layer cascade in a path aggregation network to multiplex shallow features. Notably, the CA-PANet combines deep and shallow features to enhance the feature fusion of defects at different scales, improve the feature representation of defects, and increase the defect detection accuracy. The low computational cost of the lightweight CA ensures the real-time performance of the model. Experimental results indicate that the mean average precision(mAP) of the YOLOv5 model combining DCNv2 and CA can reach 95.4%, which is 3% higher than that of the YOLOv5 model and 1.4% higher than that of the YOLOX model. The improved YOLOv5 model can achieve a frame rate of up to 51 frames per second(FPS), meeting industrial real-time requirements. Compared with other algorithms, the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells, satisfying the demand for real-time, high-precision defect detection under industrial conditions in photovoltaic power plants.
摘要:Object intrusion is among the primary causes of railway accidents. Typically, traditional deep-learning methods require numerous samples for network training; however, intrusion samples in railway settings are scarce and difficult to obtain. Thus, in this paper, a railway few-shot intruding-object detection method based on an improved metric meta-learning network is proposed. To better exploit the features of intruding objects during classification, a feature-extraction network based on the channel attention mechanism is proposed. A network based on fine-tuning of the class center is proposed for class-center correction to solve the problem of individual samples deviating in the feature space of insufficient samples. Additionally, a central correlation loss function based on the center loss and cross entropy is constructed for few-shot network training to improve the compactness of the same-class feature distribution in the feature space. In experiments on a public few-shot dataset called miniImageNet, the accuracy of the proposed method is 7.31% higher than the optimal accuracy of the classical few-shot learning model. In five-way five-shot ablation experiments using a railway dataset, the proposed channel attention mechanism and center-related loss function increase the mean average precision (mAP) by 0.86% and 1.91%, respectively. Additionally, the center fine-tuning and pretraining increase the mAP by 3.05% and 6.70%, respectively, and the total mAP improvement is 7.90%.
关键词:few-shot learning;metric meta learning;railway clearance intrusion;object detection;attention mechanism
摘要:To improve the recovery ability of polarization dehazing algorithms in fog scenes, a polarization image dehazing algorithm based on polarization optimization and atmospheric light correction is proposed. First, according to the brightness distribution of the fog scene, the fog image was decomposed into bright residuals and dark residuals via guided filtering. Second, to optimize the degree of polarization, the degrees of polarization corresponding to the bright and dark residuals were increased and decreased, respectively. This optimized degree of polarization can blur the atmospheric light image. The difference value of the degree of polarization in the residuals was used to correct the atmospheric light for ensuring its intensity range met the atmospheric degradation model. Experiments indicated that the contrast ratio was 3.07 times that in original hazy images after dehazing and that the entropy and standard deviation of dehazed images were increased by 9.21% and 61.86%, respectively. In environments with different concentrations of simulated fog, the proposed algorithm achieved excellent SSIM, CIEDE2000, and PSNR values. Compared with the state-of-art dehazing algorithms, the effect of the proposed algorithm was obvious, and it recovered the scene details efficiently.
关键词:image dehazing;degree of polarization optimization;blurry atmospheric light image;correctness of atmosphere light;guided filter residuals
摘要:When a gray-level image is affected by different factors, such as the size ratio of the target to the background, noise, or random details, its gray-level histogram exhibits peakless, unimodal, bimodal, or multimodal patterns. To deal with the issue of automatic threshold selection in these four situations within a unified framework, an automatic threshold selection method using the exponential Rényi entropy under the multi-scale product in the stationary wavelet domain is proposed. First, stationary wavelet multi-scale transformation is applied to the original gray-level image in the horizontal, vertical, and diagonal directions, and a fused image is constructed via the multi-scale multiplication of high-frequency sub-bands in each direction. Then, the fused image is sampled by the inner and outer contour image to construct a one-dimensional gray-level histogram. Finally, the exponential Rényi entropy corresponding to the constructed histogram is calculated, and the threshold corresponding to the maximum exponential Rényi entropy is taken as the final threshold. The proposed method was compared with four automatic threshold segmentation methods, two clustering segmentation methods, and two active contour segmentation methods. The experimental results for 16 synthetic images and 50 real-world images indicated that with regard to the segmentation accuracy, the proposed method outperformed the second-best method by 41.2% and 20.8% in terms of the average Matthews correlation coefficient for the synthetic and real-world images, respectively. Although the proposed method has no advantage with regard to computational efficiency, it has more robust segmentation adaptability and a higher segmentation accuracy than the other eight segmentation methods.
摘要:Classifying and locating objects of interest in Thangka images can help people understand the rich semantic information of Thangka and promote cultural inheritance. To address the problems of insufficient Thangka image samples, the complex background, the occlusion of detection targets, and the low detection accuracy, this paper proposes a few-shot object detection algorithm for Thangka images that combines multi-scale context information and dual attention guidance. First, a new multi-scale feature pyramid is constructed to learn the multi-level features and contextual information of Thangka images and improve the ability of the model to discriminate multi-scale targets. Second, a dual attention guidance module is added at the end of the feature pyramid to improve the ability of the model to represent key features while reducing the impact of noise. Finally, Rank&Sort Loss is used to replace the cross-entropy classification loss, which simplifies the model training process and increases the detection accuracy. Experimental results indicate that the proposed method achieved a mean average precision of 19.7% and 11.2% in 10-shot experiments using a Thangka dataset and the COCO dataset, respectively.