- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images
摘要： Ship detection plays a crucial role in remote sensing image processing, which has drawn great attention in recent years. A novel neural network architecture named squeeze excitation skip-connection path networks (SESPNets) is proposed. A bottom-up path is added to feature pyramid network to improve feature extraction capability, and path-level skip-connection structure is firstly proposed to enhance information flow and reduce parameter redundancy. Also, squeeze excitation module is adopted, which can adaptively recalibrate channel-wise feature responses by adding an extra branch after each shortcut path connection block. The multi-scale fused region of interest (ROI) align is then proposed to obtain more accurate and multi-scale proposals. Finally, soft-non-maximum suppression is utilized to overcome the problem of non-maximum suppression (NMS) in ship detection. As demonstrated in the experiments, it can be seen that the SESPNets model has achieved the state-of-the-art performance, which shows the effectiveness of proposed method.
关键词： Skip-connection path networks,Squeeze excitation,Ship detection,Optical remote sensing images,Deep learning
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement
摘要： The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.
关键词： spatial-pyramid,deep learning,optical flow
更新于2025-09-23 15:22:29
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[ACM Press the 2nd International Symposium - Chengdu, China (2018.10.13-2018.10.14)] Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine - ISICDM 2018 - Optic Disc and Fovea Detection Using Multi-Stage Region-Based Convolutional Neural Network
摘要： Detection of the optic disc (OD) and fovea in retinal images is an important step for automated detection of retinal disease in digital color photographs of the retina. Together with the vasculature, the optic disc and the fovea are the most important anatomical landmarks on the posterior pole of the retina. In this work, we presented a multi-stage region-based convolutional neural network for optic disc and fovea detection. In the first stage, standard faster-RCNN and SVM were employed for OD segmentation. In the second stage, the relative position information (RPI)-based faster-RCNN was proposed for fovea detection. The experimental result showed the average Euclidean distance with ground truth were 32.6 and 52 pixels for OD and fovea, respectively. The average Jaccard and dice index for OD segmentation were 0.8018 and 0.8873, respectively. The RPI-based faster-RCNN outperformed the standard network.
关键词： Deep Learning,Faster-RCNN,Fundus Image,Optic Disc and Fovea Detection
更新于2025-09-23 15:22:29
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Deep Learning-Based Automated Classification of Multi-Categorical Abnormalities From Optical Coherence Tomography Images
摘要： Purpose: To develop a new intelligent system based on deep learning for automatically optical coherence tomography (OCT) images categorization. Methods: A total of 60,407 OCT images were labeled by 17 licensed retinal experts and 25,134 images were included. One hundred one-layer convolutional neural networks (ResNet) were trained for the categorization. We applied 10-fold cross-validation method to train and optimize our algorithms. The area under the receiver operating characteristic curve (AUC), accuracy and kappa value were calculated to evaluate the performance of the intelligent system in categorizing OCT images. We also compared the performance of the system with results obtained by two experts. Results: The intelligent system achieved an AUC of 0.984 with an accuracy of 0.959 in detecting macular hole, cystoid macular edema, epiretinal membrane, and serous macular detachment. Specifically, the accuracies in discriminating normal images, cystoid macular edema, serous macular detachment, epiretinal membrane, and macular hole were 0.973, 0.848, 0.947, 0.957, and 0.978, respectively. The system had a kappa value of 0.929, while the two physicians’ kappa values were 0.882 and 0.889 independently. Conclusions: This deep learning-based system is able to automatically detect and differentiate various OCT images with excellent accuracy. Moreover, the performance of the system is at a level comparable to or better than that of human experts. This study is a promising step in revolutionizing current disease diagnostic pattern and has the potential to generate a significant clinical impact.
关键词： artificial intelligence,deep learning,optical coherence tomography,ResNet
更新于2025-09-23 15:22:29
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Separable-spectral convolution and inception network for hyperspectral image super-resolution
摘要： Due to the limitation of the imaging system, it is hard to get Hyperspectral Image (HSI) with very high spatial resolution. Super-Resolution (SR) is a handling missing data technology to restore high-frequency information from the low-resolution image, can be used to solve this problem. Recently, Deep Learning (DL) has achieved great performance in computer vision, including SR. However, most DL-based HSI SR methods neglect the spectral disorder caused by normal 2D convolution. This paper proposes a novel end–end deep learning-based network named Separable-Spectral and Inception Network (SSIN) for HSI SR. In SSIN, the feature extraction module independently extracts features of each band image, and then these features are fused together to further exploit residual image by using feature fusion module. In reconstruction module, a multi-path connection is built to obtain features of different levels to restore high spatial resolution image in a coarse-to-fine manner. Experiments are implemented on two datasets include both indoor and airborne HSIs, and the performances of SSIN are evaluated in different conditions. Experimental results show that adding several separable spectral convolutions and multi-path connection in a deep network can greatly improve the SR performance, and SSIN achieves higher accuracy and better visualization compare with other methods.
关键词： Hyperspectral Image,Separable-spectral convolution,Deep learning,Super-resolution,Multi-path reconstruction
更新于2025-09-23 15:22:29
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images
摘要： Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.
关键词： neural network,microscopic fluorescence imaging,myelin,deep learning,LeNet
更新于2025-09-23 15:22:29
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DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs
摘要： In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. Design: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0e5) using bilateral CFP. Participants: DeepSeeNet was trained on 58 402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. Methods: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. Main Outcome Measures: Overall accuracy, specificity, sensitivity, Cohen’s kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. Results: DeepSeeNet performed better on patient-based classification (accuracy ? 0.671; kappa ? 0.558) than retinal specialists (accuracy ? 0.599; kappa ? 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). Conclusions: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.
关键词： deep learning,age-related macular degeneration,automated classification,AREDS Simplified Severity Scale,color fundus photographs
更新于2025-09-23 15:22:29
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Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
摘要： As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long–short-term memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.
关键词： convolutional neural networks,deep learning,long-short term memory,Solar power forecasting
更新于2025-09-23 15:22:29
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Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting
摘要： A deep recurrent neural network with long short-term memory units (DRNN-LSTM) model is developed to forecast aggregated power load and the photovoltaic (PV) power output in community microgrid. Meanwhile, an optimal load dispatch model for grid-connected community microgrid which includes residential power load, PV arrays, electric vehicles (EVs), and energy storage system (ESS), is established under three different scheduling scenarios. To promote the supply-demand balance, the uncertainties of both residential power load and PV power output are considered in the model by integrating the forecasting results. Two real-world data sets are used to test the proposed forecasting model, and the results show that the DRNN-LSTM model performs better than multi-layer perception (MLP) network and support vector machine (SVM). Finally, particle swarm optimization (PSO) algorithm is used to optimize the load dispatch of grid-connected community microgrid. The results show that EES and the coordinated charging mode of EVs can promote peak load shifting and reduce 8.97% of the daily costs. This study contributes to the optimal load dispatch of community microgrid with load and renewable energy forecasting. The optimal load dispatch of community microgrid with deep learning based solar power and load forecasting achieves total costs reduction and system reliability improvement.
关键词： community microgrid,load forecasting,deep learning,Optimal load dispatch,solar power
更新于2025-09-23 15:22:29
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Semantic segmentation of high spatial resolution images with deep neural networks
摘要： Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.
关键词： pyramid pooling,deep learning,global context information,high-resolution image segmentation,residual network
更新于2025-09-23 15:22:29