Shi-Dan Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shi-Dan Sun has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Cheng-Fu Yang
Department of Chemical and Materials Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan
1 shared publication - Jiachun Zheng
School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
1 shared publication - Jing Liu
School of Ocean Information Engineering, Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing, Jimei University, Xiamen 361021, China
1 shared publication - Yi-Qi Huang
Navigation Institute, Jimei University, Xiamen 361021, China
1 shared publication
Latest Publications
- Open Access
- 28 April 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (9) , 3079
Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections
In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles during traffic flows. Therefore, we propose a single-stage deep neural network YOLOv3-DL, which is based on the Tensorflow framework to improve this problem. The network structure is optimized by introducing the idea of spatial pyramid pooling, then the loss function is redefined, and a weight regularization method is introduced, for that, the real-time detections and statistics of traffic flows can be implemented effectively. The optimization algorithm we use is the DL-CAR data set for end-to-end network training and experiments with data sets under different scenarios and weathers. The analyses of experimental data show that the optimized algorithm can improve the vehicles’ detection accuracy on the test set by 3.86%. Experiments on test sets in different environments have improved the detection accuracy rate by 4.53%, indicating that the algorithm has high robustness. At the same time, the detection accuracy and speed of the investigated algorithm are higher than other algorithms, indicating that the algorithm has higher detection performance.
- 28 April 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (9) , 3079
Open AccessOptimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections
In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles during traffic flows. Therefore, we propose a single-stage deep neural network YOLOv3-DL, which is based on the Tensorflow framework to improve this problem. The network structure is optimized by introducing the idea of spatial pyramid pooling, then the loss function is redefined, and a weight regularization method is introduced, for that, the real-time detections and statistics of traffic flows can be implemented effectively. The optimization algorithm we use is the DL-CAR data set for end-to-end network training and experiments with data sets under different scenarios and weathers. The analyses of experimental data show that the optimized algorithm can improve the vehicles’ detection accuracy on the test set by 3.86%. Experiments on test sets in different environments have improved the detection accuracy rate by 4.53%, indicating that the algorithm has high robustness. At the same time, the detection accuracy and speed of the investigated algorithm are higher than other algorithms, indicating that the algorithm has higher detection performance.