scholarly journals Optical sensing of sound fields: non-contact, quantitative, and single-shot imaging of sound using high-speed polarization camera

2016 ◽  
Author(s):  
Kenji Ishikawa ◽  
Kohei Yatabe ◽  
Yusuke Ikeda ◽  
Yasuhiro Oikawa ◽  
Takashi Onuma ◽  
...  
Keyword(s):  
High Speed ◽  
Optical Sensing ◽  
Single Shot ◽  
Sound Fields ◽  
Polarization Camera

Optical sensing of sound fields: Non-contact, quantitative, and single-shot imaging of sound using high-speed polarization camera

2016 ◽  
Vol 140 (4) ◽  
pp. 3087-3087
Author(s):  
Kenji Ishikawa ◽  
Kohei Yatabe ◽  
Yusuke Ikeda ◽  
Yasuhiro Oikawa ◽  
Takashi Onuma ◽  
...  
Keyword(s):  
High Speed ◽  
Optical Sensing ◽  
Single Shot ◽  
Sound Fields ◽  
Polarization Camera

scholarly journals High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2981 ◽  
Author(s):  
Baoqing Guo ◽  
Jiafeng Shi ◽  
Liqiang Zhu ◽  
Zujun Yu
Keyword(s):  
Intrusion Detection ◽  
High Speed ◽  
Rapid Development ◽  
Single Shot ◽  
L1 Norm ◽  
Low Level ◽  
Model Size ◽  
Public Dataset ◽  
The Mean ◽  
High Level

With the rapid development of high-speed railways, any objects intruding railway clearance will do great threat to railway operations. Accurate and effective intrusion detection is very important. An original Single Shot multibox Detector (SSD) can be used to detect intruding objects except small ones. In this paper, high-level features are deconvolved to low-level and fused with original low-level features to enhance their semantic information. By this way, the mean average precision (mAP) of the improved SSD algorithm is increased. In order to decrease the parameters of the improved SSD network, the L1 norm of convolution kernel is used to prune the network. Under this criterion, both the model size and calculation load are greatly reduced within the permitted precision loss. Experiments show that the mAP of our method on PASCAL VOC public dataset and our railway datasets have increased by 2.52% and 4.74% respectively, when compared to the original SSD. With our method, the elapsed time of each frame is only 31 ms on GeForce GTX1060.

scholarly journals Dense-RefineDet for Traffic Sign Detection and Classification

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6570
Author(s):  
Chang Sun ◽  
Yibo Ai ◽  
Sheng Wang ◽  
Weidong Zhang
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Design Method ◽  
Contextual Information ◽  
Real Life ◽  
Single Shot ◽  
Traffic Sign ◽  
Traffic Signs ◽  
High Level ◽  
Anchor Design

Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy–speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.

scholarly journals The Assisted Positioning Technology for High Speed Train Based on Deep Learning

2020 ◽  
Vol 10 (23) ◽  
pp. 8625
Author(s):  
Yali Song ◽  
Yinghong Wen
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Detection Method ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
High Speed Train ◽  
Data Set ◽  
Detection Model ◽  
Model Based ◽  
Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

scholarly journals High-speed single-shot optical focusing through dynamic scattering media with full-phase wavefront shaping

2017 ◽  
Vol 111 (22) ◽  
pp. 221109 ◽  
Author(s):  
Ashton S. Hemphill ◽  
Yuecheng Shen ◽  
Yan Liu ◽  
Lihong V. Wang
Keyword(s):  
High Speed ◽  
Single Shot ◽  
Scattering Media ◽  
Wavefront Shaping ◽  
Optical Focusing ◽  
Dynamic Scattering

scholarly journals Light-In-Flight Imaging by a Silicon Image Sensor: Toward the Theoretical Highest Frame Rate

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2247 ◽  
Author(s):  
Takeharu Etoh ◽  
Tomoo Okinaka ◽  
Yasuhide Takano ◽  
Kohsei Takehara ◽  
Hitoshi Nakano ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
High Speed ◽  
Streak Camera ◽  
Image Sensor ◽  
Pulse Neutron ◽  
Frame Rate ◽  
Neutron Tomography ◽  
Single Shot ◽  
Resolution Limit ◽  
Lifetime Measurements

Light in flight was captured by a single shot of a newly developed backside-illuminated multi-collection-gate image sensor at a frame interval of 10 ns without high-speed gating devices such as a streak camera or post data processes. This paper reports the achievement and further evolution of the image sensor toward the theoretical temporal resolution limit of 11.1 ps derived by the authors. The theoretical analysis revealed the conditions to minimize the temporal resolution. Simulations show that the image sensor designed following the specified conditions and fabricated by existing technology will achieve a frame interval of 50 ps. The sensor, 200 times faster than our latest sensor will innovate advanced analytical apparatuses using time-of-flight or lifetime measurements, such as imaging TOF-MS, FLIM, pulse neutron tomography, PET, LIDAR, and more, beyond these known applications.

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