Yichen Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yichen Wang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Fuchun Sun
School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
1 shared publication - Hao Teng
School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China
1 shared publication - Haorong Wu
School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China
1 shared publication - Xiaoxiao Li
School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
1 shared publication - Yuechao Bian
School of Mechanical Engineering, Chengdu University, Chengdu 610106, China
1 shared publication
Latest Publications
- Open Access
Development and Validation of a Vision-Based Dynamic Defect Detection and Robotic Sorting System for Small Irregular Stamped Parts
Automatic inspection of small, irregular stamped parts remains challenging due to their compact size, random orientation on conveyors, and susceptible metallic surface reflections, coupled with local, weak, and densely distributed defects. This study presents an integrated vision-based dynamic defect detection and robotic sorting system to address these issues. The framework seamlessly unifies conveyor-based image acquisition, YOLO-CGMS-based defect recognition, hand–eye coordinate mapping, and Delta robot trajectory planning. To drive real-time visual perception, the YOLO-CGMS architecture modifies the YOLOv8n baseline by incorporating C2f-CRM, GRF-SPPF, MCA, and Inner-GIoU modules, optimizing small-defect feature extraction and bounding-box regression. Evaluated on a factory-collected dataset, YOLO-CGMS achieved an mAP50 of 88.7% and an inference speed of 236.23 frames/s, using only 2.8 M parameters and 7.3 GFLOPs. Compared with YOLOv8n, mAP and execution speed increased by 5.1 percentage points and 12.18 frames/s, respectively, while the parameter size and computational cost dropped by 0.2 M and 0.8 GFLOPs. Furthermore, physical sorting capability was validated on a developed prototype platform, yielding a mean hand–eye positioning error of 2.66 mm. Across 200 dynamic trials, the system successfully sorted 161 defective workpieces, translating to an 80.5% success rate and a mean cycle time of 2.6 s per part. These findings confirm that the proposed system reliably links closed-loop visual perception with physical execution, providing a practical foundation for the automated quality control of complex stamped components.
Open AccessDevelopment and Validation of a Vision-Based Dynamic Defect Detection and Robotic Sorting System for Small Irregular Stamped Parts
Automatic inspection of small, irregular stamped parts remains challenging due to their compact size, random orientation on conveyors, and susceptible metallic surface reflections, coupled with local, weak, and densely distributed defects. This study presents an integrated vision-based dynamic defect detection and robotic sorting system to address these issues. The framework seamlessly unifies conveyor-based image acquisition, YOLO-CGMS-based defect recognition, hand–eye coordinate mapping, and Delta robot trajectory planning. To drive real-time visual perception, the YOLO-CGMS architecture modifies the YOLOv8n baseline by incorporating C2f-CRM, GRF-SPPF, MCA, and Inner-GIoU modules, optimizing small-defect feature extraction and bounding-box regression. Evaluated on a factory-collected dataset, YOLO-CGMS achieved an mAP50 of 88.7% and an inference speed of 236.23 frames/s, using only 2.8 M parameters and 7.3 GFLOPs. Compared with YOLOv8n, mAP and execution speed increased by 5.1 percentage points and 12.18 frames/s, respectively, while the parameter size and computational cost dropped by 0.2 M and 0.8 GFLOPs. Furthermore, physical sorting capability was validated on a developed prototype platform, yielding a mean hand–eye positioning error of 2.66 mm. Across 200 dynamic trials, the system successfully sorted 161 defective workpieces, translating to an 80.5% success rate and a mean cycle time of 2.6 s per part. These findings confirm that the proposed system reliably links closed-loop visual perception with physical execution, providing a practical foundation for the automated quality control of complex stamped components.