scholarly journals A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 1951 ◽  
Author(s):  
Jinmeng Rao ◽  
Yanjun Qiao ◽  
Fu Ren ◽  
Junxing Wang ◽  
Qingyun Du
Keyword(s):  
Deep Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Learning Object ◽  
Spatial Relationships ◽  
Outdoor Augmented Reality

Experimental Deep Learning Object Detection in Real-time Colonoscopies

2021 ◽  
Author(s):  
Adrian Ciobanu ◽  
Mihaela Luca ◽  
Tudor Barbu ◽  
Vasile Drug ◽  
Andrei Olteanu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Learning Object

Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study

2020 ◽  
Author(s):  
Hirofumi Watanabe ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Chiaki Kuwada ◽  
Yoshitaka Kise ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Object ◽  
Panoramic Radiographs ◽  
Maxillary Cyst ◽  
Preliminary Study

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals Deep-learning Object Detection for Resource Recycling

2020 ◽  
Vol 1583 ◽  
pp. 012011
Author(s):  
Yeong-Lin Lai ◽  
Yeong-Kang Lai ◽  
Syuan-Yu Shih ◽  
Chun-Yi Zheng ◽  
Ting-Hsueh Chuang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Object ◽  
Resource Recycling

A feasibility study on the development and use of a deep learning model to automate real-time monitoring of tumor position and assessment of interfraction fiducial marker migration in prostate radiotherapy patients

2021 ◽  
Author(s):  
Ryan Motley ◽  
Andrew L Fielding ◽  
Prabhakar Ramachandran
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Target Volume ◽  
Fiducial Marker ◽  
Learning Object ◽  
Fiducial Markers ◽  
Image Guided Radiotherapy ◽  
Reference Position ◽  
Prostate Radiotherapy ◽  
Detection Model

Abstract Purpose The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients. Methods and Materials A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient. Results On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum ± standard deviation marker migration were found to be 2.0±0.9 mm and 2.3±0.9 mm, respectively. Conclusion This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.

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