- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks
摘要： Manufacturing process defects or artificial operation mistakes may lead to solar cells having surface cracks, over welding, black edges, unsoldered areas, and other minor defects on their surfaces. These defects will reduce the efficiency of solar cells or even make them completely useless. In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks. The detection results from two different convolution neural networks, i.e., Faster R-CNN and R-FCN, are combined to improve detection precision and position accuracy. In addition, according to the inherent characteristics of the surface defects in solar cells, two other strategies are used to further improve the detection performance. First, the anchor points of the region proposal network (RPN) are set by adding multi-scale and multi-aspect regions to overcome the problem of high false negative rate caused by the limitation of anchor points. Second, in view of the subtle and concealed defects of solar cells, the hard negative sample mining strategy is used to solve the problem of low detection precision caused by the negative sample space being too large. The experimental results showed that the proposed method effectively reduced the false negative rate and the false positive rate of a single network, and it greatly improved the accuracy of the locations of defects while improving the object recall rate.
关键词： Deep learning,Defects detection,Faster R-CNN,Solar cell,R-FCN
更新于2025-09-23 15:21:01
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Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision
摘要： 2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class averages which allow us to measure 2D atomic spacings with up to 0.2 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe2-2xTe2x lattice that correspond to alternating rings of lattice expansion and contraction. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials.
关键词： scanning transmission electron microscopy,strain mapping,single-atom defects,Deep learning,fully convolutional network (FCN),2D materials
更新于2025-09-23 15:21:01
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Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks
摘要： In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, the optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. The parameters of the proposed multi-input architecture are learned by considering the consistency between the overall output and the contribution of each input image. The proposed MIFCN method is compared with the state-of-the-art denoising methods adopted on OCT images of normal and age-related macular degeneration eyes in a quantitative and qualitative manner.
关键词： Multi-input FCN,Optical Coherence Tomography (OCT),Image denoising,Fully convolutional network (FCN)
更新于2025-09-19 17:15:36
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An electrodeposited amorphous cobalt sulphide nanobowl array with secondary nanosheets as a multifunctional counter electrode for enhancing the efficiency in a dye-sensitized solar cell
摘要： Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different drawing styles and skills; (ii) distorting lines drawn on the touchpad make sketches more difficult to be recognized; (iii) the high-performance image segmentation via deep learning technologies needs enormous annotated sketch datasets during the training stage. In this paper, we propose a Sketch-target deep FCN Segmentation Network(SFSegNet) for automatic free-hand sketch segmentation, labeling each sketch in a single object with multiple parts. SFSegNet has an end-to-end network process between the input sketches and the segmentation results, composed of 2 parts: (i) a modified deep Fully Convolutional Network(FCN) using a reweighting strategy to ignore background pixels and classify which part each pixel belongs to; (ii) affine transform encoders that attempt to canonicalize the shaking strokes. We train our network with the dataset that consists of 10,000 annotated sketches, to find an extensively applicable model to segment stokes semantically in one ground truth. Extensive experiments are carried out and segmentation results show that our method outperforms other state-of-the-art networks.
关键词： deep learning,FCN,sketch segmentation,object segmentation
更新于2025-09-16 10:30:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - MFCNET: End-to-End Approach for Change Detection in Images
摘要： Change detection is an important task in computer vision and video processing. Due to unimportant or nuisance forms of change, traditional methods require sophisticated image pre-processing and possibly manual interaction. In this work, we propose an end-to-end approach for change detection to identify temporal changes in multiple images. Our approach feeds a pair of images into a deep convolutional neural network combining the model of MatchNet [1] and the Fully Convolutional Network [2] modified to reduce the number of parameters. We train and evaluate the proposed approach using a subset of frames from the Change Detection challenge 2014 dataset (CDnet2014). Experimental evaluation comparing the performance of the proposed approach with several known approaches shows that the proposed approach outperforms existing methods.
关键词： MatchNet,deep neural network,FCN,MFCNet,Change detection
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
摘要： This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
关键词： deep-learning,Landsat 8,FCN,image segmentation,U-Net,remote sensing,CNN,Cloud detection
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Deep Learning Based Bioresorbable Vascular Scaffolds Detection in IVOCT Images
摘要： Bioresorbable Vascular Scaffolds (BVS) are currently one of the most frequently-used type of stent during percutaneous coronary intervention. It’s very important to conduct struts malapposition analysis during operation. Currently, BVS malapposition analysis in intravascular optical coherence tomography (IVOCT) images is mainly conducted manually, which is labor intensive and time consuming. In our previous work, a novel framework was presented to automatically detect and segment BVS struts for malapposition analysis. However, limited by the detection performance, the framework faced some challenges under complex background. In this paper, we proposed a robust BVS struts detection method based on Region-based Fully Convolutional Network (R-FCN). The detection model mainly consisted of two modules: 1) a Region Proposal Network (RPN), used to extract struts region of interest (ROIs) in the image and, 2) a detection module, used to classify the ROIs and regress a bounding box for each ROI. The network was initialized by pre-trained ImageNet model and then trained based on our labeled data which contained 1231 IVOCT images. Tested on a total of 480 IVOCT images with 4096 BVS struts, our method achieved 97.9% true positive rate with 4.79% false positive rate. It concludes that the proposed method is efficient and robust for BVS struts detection.
关键词： struts detection,IVOCT,R-FCN,Bioresorbable Vascular Scaffolds,malapposition analysis
更新于2025-09-04 15:30:14