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Chanin Joochim. Object detection of pedestrian crossing accident using deep convolutional neural networks. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
Object detection of pedestrian crossing accident using deep convolutional neural networks
Name: Anan Yasamorn
Organization :
King Mongkut's University of Technology North Bangkok. Department of Mechanical Engineering
Email :
anan.y@email.kmutnb.ac.th
Name: Athasit Wongcharoen
Organization :
King Mongkut's University of Technology North Bangkok. Department of Mechanical Engineering
Email :
athasit.w@cit.kmutnb.ac.th
Name: Chanin Joochim
Organization :
King Mongkut's University of Technology North Bangkok. Department of Mechanical Engineering
Email :
chanin.j@cit.kmutnb.ac.th
LCSH:
Pedestrian crosswalks
Abstract:
Pedestrian crossing accident recently, people injured in traffic while crossing the crosswalk. As an accident happened other caution warning systems may not be in time to safe life. Various deep learning techniques are based on a deep convolutional neural network (D-CNN) these methods are capable to fulfill object detection applications. This paper proposes the pedestrian crossing accident dataset for the detection of pedestrian crossing accidents using a video camera of front cars or dash-camera and a few CCTV videos. It presents the performance comparison between the two state-of-the-art CNN algorithms approach Faster R-CNN and YOLOv3, in the context capability to correctly classify accident, fall-down, and out-of-frame classes. This paper demonstrates that Faster R-CNN outperforms YOLOv3 with a better detection in accuracy. However, the conclusion of the paper is that YOLOv3 outperforms speed to detection and has good accuracy same time able to use in real-time detection.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2023-01-19
Issued:
2023-01-19
บทความ/Article
application/pdf
BibliograpyCitation :
In King Mongkut's University of Technology North Bangkok. Science and Technology Research Institute. 2022 Research, Innovation, and Innovation Congress (RI2C 2022) (pp.297-303). Bangkok : Science and Technology Research Institute
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.021157 วินาที
Anan Yasamorn
| Title | Contributor | Type |
|---|---|---|
| Object detection of pedestrian crossing accident using deep convolutional neural networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anan Yasamorn;Athasit Wongcharoen;Chanin Joochim | บทความ/Article |
Athasit Wongcharoen
| Title | Contributor | Type |
|---|---|---|
| Object detection of pedestrian crossing accident using deep convolutional neural networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anan Yasamorn;Athasit Wongcharoen;Chanin Joochim | บทความ/Article |
Chanin Joochim
| Title | Contributor | Type |
|---|---|---|
| Object detection of pedestrian crossing accident using deep convolutional neural networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anan Yasamorn;Athasit Wongcharoen;Chanin Joochim | บทความ/Article | |
| Automatic sorting machine using industrial robot and digital image processing
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chanin Joochim;Phongsak Keeratiwintakorn | บทความ/Article |