Sailei Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sailei Wang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Dahang Wan
School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei, China
2 shared publications - Rongsheng Lu
School of Instrumental Science and Optoelectronic Engineering, Hefei University of Technology, Hefei, China
2 shared publications - Bingtao Hu
School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei, China
1 shared publication - Mingtao Fang
School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei, China
1 shared publication - Siyuan Shen
School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, People’s Republic of China
1 shared publication - Ting Xu
School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei 230009, China
1 shared publication
Latest Publications
- 5 November 2024
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 16 (5-6) , 3097-3112
Bicrack: a bilateral network for real-time crack detection
- 5 November 2024
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 16 (5-6) , 3097-3112
Bicrack: a bilateral network for real-time crack detection
- Open Access
- 20 January 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (3) , 614
YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images
Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.
- 20 January 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (3) , 614
Open AccessYOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images
Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.