- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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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Convolution neural network-based time-domain equalizer for DFT-Spread OFDM VLC system
摘要： This paper presents a novel time-domain equalizer for visible light communication (VLC) system using machine learning (ML) method. In this work, we employ discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) as modem scheme and convolution neural network (CNN) as kernel processing unit of equalizer. After estimating channel state information (CSI) from training sequence, the proposed equalizer recovers transmitted symbols according to the estimated CSI. Numerical simulations indicate that the equalizer can significantly enhance bit error rate (BER) performance. For example, when signal-to-noise ratio (SNR) is 20dB and 16/32/64-quadrature amplitude modulation (QAM) is exploited, original BER is about 0.5 while the BER after recovery achieves 10?5, which is much lower than forward error correction (FEC) limit 3.8×10?3. This work promotes the application of ML in VLC domain. To the best of our knowledge, this is the first time a CNN-based equalizer has been explored.
关键词： Machine learning (ML),Discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM),Visible light communication (VLC),Convolution neural network (CNN)
更新于2025-09-10 09:29:36
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Recognition of incorrect assembly of internal components by X-ray CT and deep learning
摘要： It is important to make sure that all components of a complex product are assembled correctly. Because in many cases, some components are enclosed in an opaque shell, x-ray imaging is currently used to extract their characteristics and match prior-known ones. However, x-ray imaging is not very robust in recognition of incorrect assembly of internal components, because some of them may overlap. To solve this problem, we propose a new method to detect internal component assembly fault, by x-ray computed tomography (CT) and convolutional neural network (CNN). Multi-view imaging is implemented by mechanical rotation of a product in respect with an x-ray CT machine to capture multiple projection information on each internal component, and then the component can be recognized by making use of deep learning. A CNN model is trained to classify the internal components and give the coordinates of each component. Based on the CNN recognition results and the CT projection sinogram, a projection corresponding to a reference in a projection data set of a standard product can be found. By comparing and matching the locations of each component, transposition or dislocation can be recognized. Both simulation and experiment show that this new method can effectively identify incorrect assembly, missing assembly, transposition, and other problems, improving the product quality.
关键词： Projection sinogram,Assembly recognition,Convolution neural network (CNN),x-ray CT
更新于2025-09-04 15:30:14