Investigation of open cylindrical multilayer waveguides filled with biological tissue

Sledevič, Tomyslav; Plonis, Darius; Katkevičius, Andrius; Krukonis, Audrius

doi:10.1109/aieee.2017.8270546

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Furthermore, in recent years, a new wave of outstanding deep learning models has emerged in the field of object detection. These include end-to-end object detection models such as DINO [38], based on the DETR architecture; DiffusionDet [39], which pioneers the application of diffusion models in object detection frameworks; EfficientDet [40], a scalable detection architecture designed under practical computational resource constraints with higher precision and efficiency; and the latest version of the YOLO series, YOLOv8 [41], among others. While these models have distinct architectural designs, they all demonstrate extremely high levels of accuracy and have achieved prominent rankings within the field.…”

Section: Introductionmentioning

confidence: 99%

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

Zou,

Xu,

et al. 2023

IJGI

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Augmented reality (AR) technology enables paper maps to dynamically express three-dimensional geographic information, realizing the fusion of virtual and real information. However, in the current mainstream AR development software, the virtual information usually consists of prefabricated components (prefabs), and the content creation for AR maps heavily relies on manual prefabrication. It leads to repetitive and error-prone prefabrication work, which restricts the design of the dynamic, interactive functions of AR maps. To solve this problem, this paper explored the possibility of automatically generating AR map prefabs using object detection models to establish a data conversion interface from paper maps to AR maps. First, we compared and analyzed various object detection models and selected YOLOv8x to recognize map point symbols. Then, we proposed a method to automatically generate AR map prefabs based on the predicted bounding boxes of the object detection model, which could generate prefabs with corresponding categories and positional information. Finally, we developed an AR map prototype system based on Android mobile devices. We designed an interaction method for information queries in the system to verify the effectiveness of the method proposed in this paper. The validation results indicate that our method can be practically applied to the AR map prefabrication process and can quickly generate AR map prefabs with high information accuracy. It alleviated the repetitive workload established through the manual prefabrication method and had specific feasibility and practicality. Moreover, it could provide solid data support for developing dynamic interactive functions of AR maps.

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Section: Introductionmentioning

confidence: 99%

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

Zou,

Xu,

et al. 2023

IJGI

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show abstract

“…Over the years, image processing and recognition have become mainstream target detection methods and the accuracy and speed of target detection have improved significantly. Such as the two-stage detector Faster-RNN [2], the single-stage detector RetinaNet [3], YOLOv5 [4], YOLOv7 [6]- [11] and YOLO8 [12], which improve network detection accuracy by increasing network depth. At present, intelligent visual retail commodity detection technology requires both high precision and high speed.…”

Section: Introductionmentioning

confidence: 99%

Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance

Xie,

Dong

et al. 2023

IEEE Access

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Although traditional convolutional neural networks (CNN) have been significantly improved for target detection, they cannot be completely applied to objects with occlusions in commodity detection. Therefore, we propose a target detection method based on an improved YOLOv5 model and an improved attention mechanism algorithm is proposed to solve the commodity occlusion problem. This method improves the traditional YOLO deep convolution network, features a more detailed BiFPN layer, and performs lightweight two-way feature fusion, where the multidimensional features of the commodities are convolved and fused, thus improving the overall detection speed and accuracy of the YOLO-R algorithm. Feature entropy is introduced to the attention channel to restrict the threshold value and obtain the global information of the occlusion target. The global information obtained is fused with a bidirectional feature pyramid layer to enhance the robustness of the features. This method could accurately and quickly detect the occluded commodities and the detection accuracy has been greatly improved. Experiments show that the improved YOLO-R model can improve the accuracy and speed of commodity detection, and can achieve good results in objective evaluation. The average accuracy of commodity detection on the selfmade product dataset is up to 97.80%, and the detection rate is 22.72F/s. Therefore, the method in this paper has high detection accuracy and fast detection speed.INDEX TERMS Lightweight feature fusion, attention channel, feature entropy, bidirectional feature.

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Investigation of the Multilayer Semiconductor Waveguides in Sub-THz Range

Rusen

Katkevičius

Plonis

2021

2021 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW)

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Investigation of open cylindrical multilayer waveguides filled with biological tissue

Cited by 3 publications

References 11 publications

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

Detection of Commodities Based on Multi-Feature Fusion and Attention Screening by Entropy Function Guidance

Investigation of the Multilayer Semiconductor Waveguides in Sub-THz Range

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