Fengjun Hou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Fengjun Hou has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Dongchen Fan
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
1 shared publication - Jinghan Lu
China Agricultural University, Beijing 100083, China
1 shared publication - Junyu Zhou
China Agricultural University, Beijing 100083, China
1 shared publication - Lin Li
China Agricultural University, Beijing 100083, China
1 shared publication - Weifan Lin
China Agricultural University, Beijing 100083, China
1 shared publication - Xinyuan Fan
China Agricultural University, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
- 14 September 2023
- Published by MDPI AG in Agriculture
- Vol. 13 (9) , 1812
A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques
With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on Transformer and super-resolution sampling techniques is introduced, aiming to enhance identification accuracy under challenging conditions. The Transformer model was found to effectively capture spatial dependencies in images, while the super-resolution sampling technique was employed to restore image details for subsequent identification processes. The experimental results demonstrated that this approach exhibited significant advantages across various pest image datasets, achieving Precision, Recall, mAP, and FPS scores of 0.97, 0.95, 0.95, and 57, respectively. Especially in the presence of low resolution and noise, this method was capable of performing pest identification with high accuracy. Furthermore, an adaptive optimizer was incorporated to enhance model convergence and performance. Overall, this study offers an efficient and accurate method for pest detection and identification in practical applications, holding significant practical value.
- 14 September 2023
- Published by MDPI AG in Agriculture
- Vol. 13 (9) , 1812
Open AccessA Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques
With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on Transformer and super-resolution sampling techniques is introduced, aiming to enhance identification accuracy under challenging conditions. The Transformer model was found to effectively capture spatial dependencies in images, while the super-resolution sampling technique was employed to restore image details for subsequent identification processes. The experimental results demonstrated that this approach exhibited significant advantages across various pest image datasets, achieving Precision, Recall, mAP, and FPS scores of 0.97, 0.95, 0.95, and 57, respectively. Especially in the presence of low resolution and noise, this method was capable of performing pest identification with high accuracy. Furthermore, an adaptive optimizer was incorporated to enhance model convergence and performance. Overall, this study offers an efficient and accurate method for pest detection and identification in practical applications, holding significant practical value.