Fengshan Zou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Fengshan Zou has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
Daokui Qu
1 shared publication- Hongyan Liu
Beijing Fuyouhua Intelligent Technology Company Ltd, Beijing, China
1 shared publication - Jilai Song
Department of Information Science and Technology, Ocean University of China, Qingdao, China
1 shared publication - Kai Jia
Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
1 shared publication - Li Jun Yang
Shaanxi Xueqian Normal University, Xi'an 710100, China
1 shared publication - Li Zhang
School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), China and Shandong Institutes of Industrial Technology, China
1 shared publication
Latest Publications
- Open Access
- 21 October 2022
- Published by Association for Computing Machinery (ACM)
- p. 1624-1630
Improved Algorithm for YOLOX-S Object Detection Based on Diverse Branch Block (DBB)
The YOLO series of algorithms are commonly used in the field of object detection, and YOLOX, as one of them, is an improvement on YOLOv5, which improves the detection accuracy but decreases the inference speed. In order to further improve the detection accuracy of YOLOX and solve the problem of slow inference speed, the YOLOX-DBB algorithm is proposed on the basis of YOLOX-S with reference to the idea of structural re-parameterization. Experimental results on the VOC dataset showed that YOLOX-DBB achieved 62.88% mAP0.5:0.95, a 3.47% improvement over YOLOX-S; meanwhile, YOLOX-DBB achieved 82.46% mAP0.5, an improvement of 1.02%. In inference speed, YOLOX-DBB takes only 10.36(ms) to infer a picture, while YOLOX-S takes 12.55(ms) in the same environment. Therefore, the new network has improved detection accuracy, and the inference speed is also faster than the original network. Finally, YOLOX-DBB was applied to the art craft surface defect detection task, and the results showed that it still had better indicators than YOLOX-S.
- 21 October 2022
- Published by Association for Computing Machinery (ACM)
- p. 1624-1630
Open AccessImproved Algorithm for YOLOX-S Object Detection Based on Diverse Branch Block (DBB)
The YOLO series of algorithms are commonly used in the field of object detection, and YOLOX, as one of them, is an improvement on YOLOv5, which improves the detection accuracy but decreases the inference speed. In order to further improve the detection accuracy of YOLOX and solve the problem of slow inference speed, the YOLOX-DBB algorithm is proposed on the basis of YOLOX-S with reference to the idea of structural re-parameterization. Experimental results on the VOC dataset showed that YOLOX-DBB achieved 62.88% mAP0.5:0.95, a 3.47% improvement over YOLOX-S; meanwhile, YOLOX-DBB achieved 82.46% mAP0.5, an improvement of 1.02%. In inference speed, YOLOX-DBB takes only 10.36(ms) to infer a picture, while YOLOX-S takes 12.55(ms) in the same environment. Therefore, the new network has improved detection accuracy, and the inference speed is also faster than the original network. Finally, YOLOX-DBB was applied to the art craft surface defect detection task, and the results showed that it still had better indicators than YOLOX-S.
- Open Access
- 12 June 2020
- Published by Optica Publishing Group in Optics Express
- Vol. 28 (13) , 19058-19073
Approach for accurate calibration of RGB-D cameras using spheres
RGB-D cameras (or color-depth cameras) play key roles in many vision applications. A typical RGB-D camera has only rough intrinsic and extrinsic calibrations that cannot provide the accuracy required in many vision applications. In this paper, we propose a novel and accurate sphere-based calibration framework for estimating the intrinsic and extrinsic parameters of color-depth sensor pair. Additionally, a method of depth error correction is suggested, and the principle of error correction is analyzed in detail. In our method, the feature extraction module can automatically and reliably detect the center and edges of the sphere projection, while excluding noise data and outliers, and the projection of the sphere center on RGB and depth images is used to obtain a closed solution of the initial parameters. Finally, all the parameters are accurately estimated within the framework of nonlinear global minimization. Compared to other state-of-the-art methods, our calibration method is easy to use and provides higher calibration accuracy. Detailed experimental analysis is performed to support our conclusions.
- 12 June 2020
- Published by Optica Publishing Group in Optics Express
- Vol. 28 (13) , 19058-19073
Open AccessApproach for accurate calibration of RGB-D cameras using spheres
RGB-D cameras (or color-depth cameras) play key roles in many vision applications. A typical RGB-D camera has only rough intrinsic and extrinsic calibrations that cannot provide the accuracy required in many vision applications. In this paper, we propose a novel and accurate sphere-based calibration framework for estimating the intrinsic and extrinsic parameters of color-depth sensor pair. Additionally, a method of depth error correction is suggested, and the principle of error correction is analyzed in detail. In our method, the feature extraction module can automatically and reliably detect the center and edges of the sphere projection, while excluding noise data and outliers, and the projection of the sphere center on RGB and depth images is used to obtain a closed solution of the initial parameters. Finally, all the parameters are accurately estimated within the framework of nonlinear global minimization. Compared to other state-of-the-art methods, our calibration method is easy to use and provides higher calibration accuracy. Detailed experimental analysis is performed to support our conclusions.