Apple Maturity and Defect Detection Using Improved YOLOv5
- Resource Type
- Conference
- Authors
- Guo, Haodong; Chen, Chaobo; Zhao, Suping; Song, Xiaohua; Yan, Kun; Zhang, Binbin
- Source
- 2023 9th International Conference on Mechanical and Electronics Engineering (ICMEE) Mechanical and Electronics Engineering (ICMEE), 2023 9th International Conference on. :81-86 Nov, 2023
- Subject
- Components, Circuits, Devices and Systems
Robotics and Control Systems
YOLO
Knowledge engineering
Cognition
Numerical models
Robots
Autonomous vehicles
Defect detection
picking robot
apple detection
knowledge distillation
lightweight
yolov5
- Language
Unmanned vehicles with arms are currently widely employed for apple picking in orchards. The primary operation is the detection of apple maturity and surface defect. An improved Y 010v5 is proposed to solve the problems of low detection rate and slow model reasoning speed. Firstly, a lighter MobileNetv3_Block network is used to replace the original network backbone. Secondly, the knowledge distillation is introduced to improve algorithm precision, where the yolov5x network acts as a teacher model. Numerical experiments are carried out to validate the improved YOLOV5. Comparisons show that the proposed method improves the detection accuracy and reduces the calculation.