Pengbo Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Pengbo Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chentao He
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication - Jierui Chen
College of Jinshan, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication - Qiming Song
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication - Qiyan Sun
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication - Xiangzeng Kong
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
1 shared publication - Zhicong Luo
Fujian Agriculture and Forestry University
1 shared publication
Latest Publications
- Open Access
A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices
In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.
Open AccessA Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices
In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.