Deep convolutional neural network for real time object detection using tensor flow

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

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Real-Time Facial Expression Recognition Using Deep Convolutional Neural Network

2019 IEEE International Conference on Mechatronics and Automation (ICMA) ◽

10.1109/icma.2019.8816322 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yuwen Zeng ◽

Nan Xiao ◽

Kaidi Wang ◽

Hang Yuan

Keyword(s):

Neural Network ◽

Facial Expression ◽

Convolutional Neural Network ◽

Real Time ◽

Facial Expression Recognition ◽

Deep Convolutional Neural Network ◽

Expression Recognition

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Design of a Scalable and Fast YOLO for Edge-Computing Devices

Sensors ◽

10.3390/s20236779 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6779

Author(s):

Byung-Gil Han ◽

Joon-Goo Lee ◽

Kil-Taek Lim ◽

Doo-Hyun Choi

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Real Time ◽

Processing Speed ◽

Edge Computing ◽

Light Weight ◽

Detection Technology ◽

Accuracy Performance ◽

Speed And Accuracy

With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% [email protected] in the MS COCO dataset than YOLOv4-tiny model.

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