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- 摘要
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- 实验方案
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[IEEE 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Guangzhou, China (2018.10.8-2018.10.12)] 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - An Efficient Recognition Method for Incomplete Iris Image Based on CNN Model
摘要： The iris of the eye is a research hot spot in the field of biometric identification because of its uniqueness, non-contact and bioactivity. The incompleteness of the iris caused by the acquisition process has brought great uncertainty to the subsequent iris region segmentation and iris code matching, thereby reducing the efficiency of iris recognition. This paper describes a deep convolution neural network model with adaptive incomplete iris preprocessing mechanism. Based on the normalization of the iris image, the incomplete iris preprocessing mechanism adopts the method of making the inner circle or the outer circle. The iris region can be segmented by the line fitting and the circle fitting method for extracting as many iris features as possible. The deep convolution neural network then uses pixel coding of Irregular iris regions to complete the iris pattern classification. The model fully utilizes the characteristics of deep learning, local feature characterization and weight sharing, and realizes the problem of using large sample to compensate the incomplete feature of local feature. The experimental results show that this method has significant accuracy improvement compared with the traditional algorithms.
关键词： iris recognition,convolution neural network,iris image normalization,algorithm
更新于2025-09-23 15:23:52
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Remote sensing images super-resolution with deep convolution networks
摘要： Remote sensing image data have been widely applied in many applications, such as agriculture, military, and land use. It is difficult to obtain remote sensing images in both high spatial and spectral resolutions due to the limitation of implements in image acquisition and the law of energy conservation. Super-resolution (SR) is a technique to improve the resolution from a low-resolution (LR) to a high-resolution (HR). In this paper, a novel deep convolution network (DCN) SR method (SRDCN) is proposed. Based on hierarchical architectures, the proposed SRDCN learns an end-to-end mapping function to reconstruct an HR image from its LR version; furthermore, extensions of SRDCN based on residual learning and multi scale version are investigated for further improvement, namely Developed SRDCN(DSRDCN) and Extensive SRDCN(ESRDCN). Experimental results using different types of remote sensing data (e.g., multispectral and hyperspectral) demonstrate that the proposed methods outperform the traditional sparse representation based methods.
关键词： Convolution neural network,Remote sensing imagery,Super-resolution
更新于2025-09-23 15:23:52
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Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images
摘要： Object detection in aerial images is widely applied in many applications. In recent years, faster region convolutional neural network shows a great improvement on object detecting in natural images. Considering the size and distribution characteristic of object in remote sensing images, the region proposal network (RPN) should be changed before being adopted. In this letter, a scale adaptive proposal network (SAPNet) is proposed to improve the accuracy of multiobject detection in remote sensing images. The SAPNet consists of multilayer RPNs which are designed to generate multiscale object proposals, and a ?nal detection subnetwork in which fusion feature layer has been applied for better multiobject detection. Comparative experimental results show that the proposed SAPNet signi?cantly improves the accuracy of multiobject detection.
关键词： region proposal network (RPN),multiobject detection,remote sensing images,Convolution neural network (CNN)
更新于2025-09-23 15:22:29
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[IEEE 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Huhhot (2018.9.14-2018.9.16)] 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - A Remote Sensing Image Key Target Recognition System Design Based on Faster R-CNN
摘要： Aiming at the problem of traditional low-level recognition of key targets in remote sensing images, a method for target detection and recognition based on Faster R-CNN is proposed. Firstly, the open source remote sensing image data set NWPU VHR-10 dataset is converted into VOC 2007 format as the training sets and test sets. Secondly, according to the training set category information, the hyper-parameters of the neural network are refined, and then the training set is trained using the Faster R-CNN neural network to generate a model. Finally, this model is used to detect unknown remote sensing images and identify important targets. The simulation results show that the method has high recognition accuracy and speed, and can provide reference for recognition of the key targets of remote sensing images.
关键词： Faster R-CNN,convolution neural network,deep learning,key target recognition,remote sensing image detection
更新于2025-09-23 15:22:29
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A Novel Patch Variance Biased Convolution Neural Network for No-Reference Image Quality Assessment
摘要： Deep Convolutional Neural Networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances to achieve better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. Experimental results show that this simple approach can achieve state-of-the-art performance as compared with well-known NR-IQA algorithms.
关键词： deep learning,no-reference image quality assessment,convolution neural network
更新于2025-09-23 15:22:29
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Robust Landmark Detection and Position Measurement Based on Monocular Vision for Autonomous Aerial Refueling of UAVs
摘要： In this paper, a position measurement system, including drogue's landmark detection and position computation for autonomous aerial refueling of unmanned aerial vehicles, is proposed. A multitask parallel deep convolution neural network (MPDCNN) is designed to detect the landmarks of the drogue target. In MPDCNN, two parallel convolution networks are used, and a fusion mechanism is proposed to accomplish the effective fusion of the drogue's two salient parts' landmark detection. Considering the drogue target's geometric constraints, a position measurement method based on monocular vision is proposed. An effective fusion strategy, which fuses the measurement results of drogue's different parts, is proposed to achieve robust position measurement. The error of landmark detection with the proposed method is 3.9%, and it is obviously lower than the errors of other methods. Experimental results on the two KUKA robots platform verify the effectiveness and robustness of the proposed position measurement system for aerial refueling.
关键词： landmark detection,multitask parallel deep convolution neural network (MPDCNN),monocular vision,position measurement,Aerial refueling
更新于2025-09-23 15:22:29
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Deep learning based automatic defect identification of photovoltaic module using electroluminescence images
摘要： The maintenance of large-scale photovoltaic (PV) power plants is considered as an outstanding challenge for years. This paper presented a deep learning-based defect detection of PV modules using electroluminescence images through addressing two technical challenges: (1) providing a large number of high-quality Electroluminescence (EL) image generation method for the limit of EL image samples; and (2) an efficient model for automatic defect classification with the generated EL image. The EL image generation approach combines traditional image processing technology and GAN characteristics. It can produce a large number of EL image samples with high resolution using a limited number of samples. Then, a convolution neural network (CNN) based model for the automatic classification of defects in an EL image is presented. CNN is used to extract the deep feature of the EL image. It can greatly increase the accuracy and efficiency of PV modules inspection and health management in comparison with the other solutions. The proposed solution is assessed through extensive experiments by using the existing machine learning models, VGG16, ResNet50, Inception V3 and MobileNet, as the comparison benchmarks. The numerical results confirm that the proposed deep learning-based solution can carry out efficient and accurate defect detection automatically using the electroluminescence images.
关键词： Automatic defect classification,Electroluminescence Images,Generative adversarial network,Convolution neural network
更新于2025-09-23 15:19:57
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A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning
摘要： Ship detection plays an important role in automatic remote sensing image interpretation. The scale difference, large aspect ratio of ship, complex remote sensing image background and ship dense parking scene make the detection task difficult. To handle the challenging problems above, we propose a ship rotation detection model based on a Feature Fusion Pyramid Network and deep reinforcement learning (FFPN-RL) in this paper. The detection network can efficiently generate the inclined rectangular box for ship. First, we propose the Feature Fusion Pyramid Network (FFPN) that strengthens the reuse of different scales features, and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships. Second, in order to get accurate ship angle information, we apply deep reinforcement learning to the inclined ship detection task for the first time. In addition, we put forward prior policy guidance and a long-term training method to train an angle prediction agent constructed through a dueling structure Q network, which is able to iteratively and accurately obtain the ship angle. In addition, we design soft rotation non-maximum suppression to reduce the missed ship detection while suppressing the redundant detection boxes. We carry out detailed experiments on the remote sensing ship image dataset, and the experiments validate that our FFPN-RL ship detection model has efficient detection performance.
关键词： feature map fusion,deep reinforcement learning,ship detection,convolution neural network
更新于2025-09-19 17:15:36
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Efficient facial expression recognition via convolution neural network and infrared imaging technology
摘要： Facial expression recognition is an important research topic in the field of human-machine interaction. Infrared imaging technology has illustrated good potential in the applications of facial expression recognition and computer vision. But the low-resolution spectral data has limited its applications, such as band overlap and random noises. To address the problems, a rapid blind restoration model with discrete beamlet transforms regularization is presented to reconstruct the infrared spectrum. To compare the sparsity between the observed infrared spectrum and ground-truth one in frequency domain, the discrete beamlet transforms is applied to analyze the different of their coefficients distributions. We propose an IR spectral deconvolution model with the sparsity coefficients regularization by L0-norm. We execute the proposed algorithm on the simulated and actual IR spectrum data, and the results demonstrate that can effectively suppress the Poisson noises and retain infrared spectral structure. The high-resolution IR spectrum can raise the recognition rate of facial expression classification task.
关键词： Regularization,Facial expression recognition,Infrared spectroscopy,Convolution neural network
更新于2025-09-16 10:30:52
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Intelligent classification of silicon photovoltaic cell defects based on eddy current thermography and convolution neural network
摘要： Defects the production process of silicon photovoltaic (Si-PV) cells are urgently needed to be detected due to their serious impact on the normal generation of PV system. In view of the shortcomings such as low defect efficiency, few detection data and high detection error rate in the existing industrial production line, the main research purpose of this study is to complete an intelligent classification method for efficient and innovative defect detection for Si-PV cells and modules. The purpose is to improve the detection efficiency of Si-PV cell, to ensure the safety and reliability of Si-PV cell production process, to achieve large number of Si-PV cell defects detection and classification. Firstly, the Eddy Current Thermography (ECT) system of Si-PV cells was established. Secondly, Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF) algorithms are compared for thermography sequences processing. Thirdly, LeNet-5, VGG-16 and GoogleNet models are compared for Si-PV cell defects classification. Finally, the results showed that the proposed method have successful application in Si-PV cell defects detection and classification.
关键词： Nondestructive testing & evaluation,Defect feature extraction,Defect classification,Convolution neural network,Silicon photovoltaic cell,Eddy current thermography
更新于2025-09-11 14:15:04