Journals >Opto-Electronic Engineering
Contents
2024
Volume: 51 Issue 10
9 Article(s)
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Research progress and development trend of electromagnetic cloaking
Ran Sun, Fei Sun, and Yichao Liu
With the advancement of electromagnetic cloaking techniques,structures that can reduce an object's radar cross-section to nearly zero have become a focal point of research and are expected to have substantial application value in areas such as military equipment cloaking,electromagnetic shielding,and invisible sensWith the advancement of electromagnetic cloaking techniques,structures that can reduce an object's radar cross-section to nearly zero have become a focal point of research and are expected to have substantial application value in areas such as military equipment cloaking,electromagnetic shielding,and invisible sensors. This paper outlines the principles,operating conditions,and performance metrics of various types of cloaking structures,clarifies the distinctions between radar cloaking and perfect cloaking,as well as between full-space and carpet cloaking. It reviews the two primary cloaking mechanisms of light bending and scattering cancellation,and provides a summary of the theoretical approaches,material types,and optimization algorithms commonly employed in the design of cloaking structures. Additionally,it compiles a review of the key theoretical studies and experimental advancements in cloaking both domestically and internationally. Finally,a summary of the main issues existing in cloaking structures is provided,and a prospective view on their future development direction is presented..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240191 (2024)
Article
Image recognition of complex transmission lines based on lightweight encoder-decoder networks
Yuntang Li, Wenkai Zhu, Hengjie Li, Juan Feng, Yuan Chen, Jie Jin, Bingqing Wang, and Xiaolu Li
To address the problems of too many parameters and much time consumption in existing recognition networks for transmission lines,a lightweight encoder-decoder network is constructed to discern complex transmission line images featured with multiple intersections quickly and accurately. The encoder is based on the firstTo address the problems of too many parameters and much time consumption in existing recognition networks for transmission lines,a lightweight encoder-decoder network is constructed to discern complex transmission line images featured with multiple intersections quickly and accurately. The encoder is based on the first 16 layers of conventional MobileNetV3 to reduce network parameters. The convolutional block attention module is used to replace the squeeze and excitation attention module to improve the network's ability to extract the feature information of transmission lines. The decoder is constructed by combining deeply separable convolution and deep atrous spatial pyramid pooling to expand the receptive field and improve the network's ability to aggregate contextual information with different scales. Moreover,the training network is sparse by using the L1 regularization method. The pruning threshold is determined according to the product of the scaling factor and the corresponding output of each channel to remove redundant channels and compress the network effectively,which improves the recognition speed of transmission lines. Experimental results demonstrate that the mean pixel accuracy,mean intersection over union ,and recognition speed of the lightweight encoder-decoder network are 92.11%,84.19%,and 41 frames per second,respectively,which are better than PSPNet,U2Net,and existing improved transmission lines recognition networks..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240158 (2024)
Dual-layer 3D terahertz metamaterial based multifunctional sensor
Guangsheng Deng, Aoran Guo, Xinqi Cheng, Jun Yang, and Fei Cai
The versatility of sensors,as a crucial part of integrated devices,is receiving increasing attention. Here,a terahertz metamaterial multifunctional sensor based on the coupling of a two-layer 3D resonant structure is introduced. The sensor consists of an upper and lower polyimide film substrate,a graphite layer attacheThe versatility of sensors,as a crucial part of integrated devices,is receiving increasing attention. Here,a terahertz metamaterial multifunctional sensor based on the coupling of a two-layer 3D resonant structure is introduced. The sensor consists of an upper and lower polyimide film substrate,a graphite layer attached to the lower polyimide film substrate,and a periodic double-layer 3D toothed coupling resonant structure between the graphite layer and the upper polyimide film substrate,which consists of a symmetric mountain-shaped structure in the lower layer and a symmetric concave structure in the upper layer. The three-dimensional metamaterial can achieve multifunctional measurements: the refractive index change of the liquid medium can be detected with high sensitivity by measuring the resonant frequency of the structure. Therefore,it is possible to detect the liquid medium with such a design. Meanwhile,in terms of micro displacement sensing,a high micro displacement measurement sensitivity can be realized in both the z-axis and y-axis directions,respectively. The 3D metamaterial sensor proposed in this paper provides an idea for the design of a functionally integrated sensor in the terahertz region..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240164 (2024)
Unsupervised light field depth estimation based on sub-light field occlusion fusion
Haoyu Li, Yeyao Chen, Zhidi Jiang, Gangyi Jiang, and Mei Yu
Light field depth estimation is an important scientific problem of light field processing and applications. However,the existing studies ignore the geometric occlusion relationship among views in the light field. By analyzing the occlusion among different views,an unsupervised light field depth estimation method based Light field depth estimation is an important scientific problem of light field processing and applications. However,the existing studies ignore the geometric occlusion relationship among views in the light field. By analyzing the occlusion among different views,an unsupervised light field depth estimation method based on sub-light field occlusion fusion is proposed. The proposed method first adopts an effective sub-light field division mechanism to consider the depth relationship at different angular positions. Specifically,the views on the primary and secondary diagonals of the light field sub-aperture arrays are divided into four sub-light fields,i.e.,top-left,top-right,bottom-left,and bottom-right. Then,a spatial pyramid pooling feature extraction and a U-Net network are leveraged to estimate the depths of the sub-light fields. Finally,an occlusion fusion strategy is designed to fuse all sub-light field depths to obtain the final depth. This strategy assigns greater weights to the sub-light field depth with higher accuracy in the occlusion region,thus reducing the occlusion effect. In addition,a weighted spatial and an angular consistency loss are employed to constrain network training and enhance robustness. Experimental results demonstrate that the proposed method exhibits favorable performance in both quantitative metrics and qualitative comparisons..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240166 (2024)
MAS-YOLOv8n road damage detection algorithm from the perspective of drones
Xiaoyan Wang, Xiyu Wang, Jie Li, Wenhui Liang, Jianhong Mou, and Churan Bi
To address the detection challenges posed by the complex backgrounds and significant variations in target scales in road damage images captured from drone aerial perspectives,a road damage detection method called MAS-YOLOv8n,incorporating a multi-branch hybrid attention mechanism,is proposed. Firstly,to address the proTo address the detection challenges posed by the complex backgrounds and significant variations in target scales in road damage images captured from drone aerial perspectives,a road damage detection method called MAS-YOLOv8n,incorporating a multi-branch hybrid attention mechanism,is proposed. Firstly,to address the problem of the residual structure in the YOLOv8n model being prone to interference,resulting in information loss,a multi-branch mixed attention (MBMA) mechanism is introduced. This MBMA structure is integrated into the C2f structure,strengthening the feature representation capabilities. It not only captures richer feature information but also reduces the impact of noise on the detection results. Secondly,to address the issue of poor detection performance resulting from significant variations in road damage morphologies,the TaskAlignedAssigner label assignment algorithm used in the YOLOv8n model is improved by utilizing ShapeIoU (shape-intersection over union),making it more suitable for targets with diverse shapes and further enhancing detection accuracy. Experimental evaluations of the MAS-YOLOv8n model on the China-Drone dataset of road damages captured by drones reveal that compared to the baseline YOLOv8n model,our model achieves a 3.1% increase in mean average precision (mAP) without incurring additional computational costs. To further validate the model's generalizability,tests on the RDD2022_Chinese and RDD2022_Japanese datasets also demonstrate improved accuracy. Compared to YOLOv5n,YOLOv8n,YOLOv10n,GOLD-YOLO,Faster-RCNN,TOOD,RTMDet-Tiny,and RT-DETR,our model exhibits superior detection accuracy and performance,showcasing its robust generalization capabilities..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240170 (2024)
GLCrowd: a weakly supervised global-local attention model for congested crowd counting
Hongmin Zhang, Qianqian Tian, Dingding Yan, and Lingyu Bu
To address the challenges of crowd counting in dense scenes,such as complex backgrounds and scale variations,we propose a weakly supervised crowd counting model for dense scenes,named GLCrowd,which integrates global and local attention mechanisms. First,we design a local attention module combined with deep convolution To address the challenges of crowd counting in dense scenes,such as complex backgrounds and scale variations,we propose a weakly supervised crowd counting model for dense scenes,named GLCrowd,which integrates global and local attention mechanisms. First,we design a local attention module combined with deep convolution to enhance local features through context weights while leveraging feature weight sharing to capture high-frequency local information. Second,the Vision Transformer (ViT) self-attention mechanism is used to capture low-frequency global information. Finally,the global and local attention mechanisms are effectively fused,and counting is accomplished through a regression token. The model was tested on the Shanghai Tech Part A,Shanghai Tech Part B,UCF-QNRF,and UCF_CC_50 datasets,achieving MAE values of 64.884,8.958,95.523,and 209.660,and MSE values of 104.411,16.202,173.453,and 282.217,respectively. The results demonstrate that the proposed GLCrowd model exhibits strong performance in crowd counting within dense scenes..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240174 (2024)
Colorectal polyp segmentation method combining polarized self-attention and Transformer
Bin Xie, Yangqian Liu, and Yulin Li
A new colorectal polyp image segmentation method combining polarizing self-attention and Transformer is proposed to solve the problems of traditional colorectal polyp image segmentation such as insufficient target segmentation,insufficient contrast and blurred edge details. Firstly,an improved phase sensing hybrid moduA new colorectal polyp image segmentation method combining polarizing self-attention and Transformer is proposed to solve the problems of traditional colorectal polyp image segmentation such as insufficient target segmentation,insufficient contrast and blurred edge details. Firstly,an improved phase sensing hybrid module is designed to dynamically capture multi-scale context information of colorectal polyp images in Transformer to make target segmentation more accurate. Secondly,the polarization self-attention mechanism is introduced into the new method to realize the self-attention enhancement of the image,so that the obtained image features can be directly used in the polyp segmentation task to improve the contrast between the lesion area and the normal tissue area. In addition,the cue-cross fusion module is used to enhance the ability to capture the geometric structure of the image in dynamic segmentation,so as to improve the edge details of the resulting image. The experimental results show that the proposed method can not only effectively improve the precision and contrast of colorectal polyp segmentation,but also overcome the problem of blurred detail in the segmentation image. The test results on the data sets CVC-ClinicDB,Kvasir,CVC-ColonDB and ETIS-LaribPolypDB show that the proposed method can achieve better segmentation results,and the Dice similarity index is 0.946,0.927,0.805 and 0.781,respectively..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240179 (2024)
A 3D reconstruction method based on multi-view fusion and hand-eye coordination for objects beyond the visual field
Botao Zhang, Zhengqiang Li, Chaohao Hua, Jialong Xie, and Qiang Lu
The dynamic depth camera has a limited single-frame field of view,and there is noise disturbance when stitching multiple frames. To deal with the aforementioned problems,a large-scale 3D target pose measurement and reconstruction method based on multi-view fusion is presented. This approach builds a hierarchical model The dynamic depth camera has a limited single-frame field of view,and there is noise disturbance when stitching multiple frames. To deal with the aforementioned problems,a large-scale 3D target pose measurement and reconstruction method based on multi-view fusion is presented. This approach builds a hierarchical model of the depth camera's performance gradient,predicts the pose with a multi-view scanning method based on point cloud normal vectors,and fits 3D models of targets with height constraints RANSAC (height constraints RANSAC,HC-RANSAC). The depth camera installed on the end of the robotic manipulator scans and measures the target from various angles,and the sampled data is utilized to reconstruct the target model in the local coordinate system. Experimental results reveal that when compared to fixed-depth cameras and classical reconstruction approaches based on pan-tilt vision,the proposed approach has a larger reconstruction field of view and higher reconstruction accuracy. It can reconstruct huge targets at a close range,and get an excellent balance between field of vision and precision..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240180 (2024)
Small target detection in sonar images with multilevel feature screening and task dynamic alignment
Yan Wang, Honghui Wang, Shudong Liu, Yan Zhang, and Zeyu Hao
To solve the problem of small target detection in sonar images,which is difficult,low precision,and prone to misdetection and omission detection,this paper proposes an improved algorithm for small target detection in sonar images based on YOLOv8s. Firstly,considering that small targets in sonar images usually have low To solve the problem of small target detection in sonar images,which is difficult,low precision,and prone to misdetection and omission detection,this paper proposes an improved algorithm for small target detection in sonar images based on YOLOv8s. Firstly,considering that small targets in sonar images usually have low contrast and are easily overwhelmed by noise,an efficient multi-level screening feature pyramid network (EMS-FPN) is proposed. Secondly,since the classification branch and localization branch of the decoupled head are independent,which will increase the number of parameters of the model,and at the same time,it is difficult to effectively adapt to the detection needs of targets of different scales,resulting in poor detection of small targets,the task dynamic alignment detection head module (TDADH) is designed. Finally,to verify the effectiveness of the model in this paper,the corresponding validation was carried out on URPC2021 and SCTD expanded sonar dataset,mAP0.5 improved by 0.3% and 1.8% compared with YOLOv8s,respectively,and the number of parameters was reduced by 22.5%. The results show that the method proposed in this paper not only improves the accuracy but also significantly reduces the number of model parameters in the task of target detection in sonar images..
Opto-Electronic Engineering
- Publication Date: Oct. 25, 2024
- Vol. 51, Issue 10, 240196 (2024)
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