Research on Multitask Deep Learning Network for Semantic Segmentation and Object Detection

2018 ◽  
pp. 708-718 ◽  
Author(s):  
Ting Rui ◽  
Feng Xiao ◽  
Jian Tang ◽  
Fukai Zhang ◽  
Chengsong Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Semantic Segmentation ◽  
Learning Network ◽  
Deep Learning Network

scholarly journals A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2501 ◽  
Author(s):  
Yanan Song ◽  
Liang Gao ◽  
Xinyu Li ◽  
Weiming Shen
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Local Information ◽  
Local Region ◽  
Feature Representation ◽  
Learning Network ◽  
Feature Representations ◽  
Encoding Method ◽  
Deep Learning Network

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

DEEP LEARNING NETWORK FOR MAIN OBJECT DETECTION COMBINING VGG NETWORK AND GRAD-CAM

2020 ◽  
Vol SP (2) ◽  
pp. 89-98
Author(s):  
Nae Joung Kwak ◽  
Sun Jin Kim ◽  
Dong Ju Kim
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Network ◽  
Deep Learning Network

scholarly journals Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Yung-Hsien Hsieh ◽  
Fang-Rong Hsu ◽  
Seng-Tong Dai ◽  
Hsin-Ya Huang ◽  
Dar-Ren Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Imaging ◽  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Breast Ultrasound ◽  
Data System ◽  
Ultrasound Images ◽  
Convolutional Network ◽  
Learning Network ◽  
Deep Learning Network

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

scholarly journals An End-to-End Deep Learning Network for 3D Object Detection From RGB-D Data Based on Hough Voting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 138810-138822 ◽  
Author(s):  
Ming Yan ◽  
Zhongtong Li ◽  
Xinyan Yu ◽  
Cong Jin
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
3D Object ◽  
Learning Network ◽  
Hough Voting ◽  
End To End ◽  
Deep Learning Network ◽  
3D Object Detection

scholarly journals Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

2017 ◽  
Author(s):  
Raja Sekhar Dheekonda ◽  
Sampad Panda ◽  
Md Nazmuzzaman khan ◽  
Mohammad Hasan ◽  
Sohel Anwar
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Sensor Fusion ◽  
Fusion Algorithm ◽  
Learning Network ◽  
Deep Learning Network
Sign in / Sign up

Export Citation Format

Share Document