Guangle Yao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Guangle Yao has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Fei Xie
College of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology, Chengdu 610059, China
1 shared publication Hailu Liao
College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China
1 shared publication- Hao Chen
School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China
1 shared publication - Honggang Chen
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
1 shared publication - Meiling Wang
College of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology, Chengdu 610059, China
1 shared publication - Ruipeng Zhu
School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China
1 shared publication
Latest Publications
- Open Access
- 20 October 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (20) , 10366
A Review of Medical Visual Question Answering
Medical Visual Question Answering (Med-VQA) integrates medical image understanding and natural-language reasoning, but strong benchmark results do not yet establish clinical reliability. This structured review focuses on representative Med-VQA models published from January 2022 through 30 September 2026, while retaining earlier methods and datasets only as historical and technical context. The revised review reports its information sources, search strategy, eligibility criteria, screening logic, and data-charting procedure. It compares classification-based and generation-based systems according to their answer-production mechanism, surveys ten public datasets, and critically interprets reported performance in light of heterogeneous data splits, preprocessing, answer vocabularies, and evaluation protocols. Particular attention is given to visual grounding, hallucination, domain generalization, experimental randomness, reproducibility, and the gap between benchmark accuracy and clinical validation. The review concludes that progress toward trustworthy Med-VQA requires evidence-grounded answers, uncertainty-aware evaluation, repeated-seed reporting, external validation, and sustained human oversight.
- 20 October 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (20) , 10366
Open AccessA Review of Medical Visual Question Answering
Medical Visual Question Answering (Med-VQA) integrates medical image understanding and natural-language reasoning, but strong benchmark results do not yet establish clinical reliability. This structured review focuses on representative Med-VQA models published from January 2022 through 30 September 2026, while retaining earlier methods and datasets only as historical and technical context. The revised review reports its information sources, search strategy, eligibility criteria, screening logic, and data-charting procedure. It compares classification-based and generation-based systems according to their answer-production mechanism, surveys ten public datasets, and critically interprets reported performance in light of heterogeneous data splits, preprocessing, answer vocabularies, and evaluation protocols. Particular attention is given to visual grounding, hallucination, domain generalization, experimental randomness, reproducibility, and the gap between benchmark accuracy and clinical validation. The review concludes that progress toward trustworthy Med-VQA requires evidence-grounded answers, uncertainty-aware evaluation, repeated-seed reporting, external validation, and sustained human oversight.
- Open Access
- 5 September 2026
- Published by Springer Nature in BMC Medical Imaging
Wavelet-refined semantic-guided network for retinal vessel segmentation
- 5 September 2026
- Published by Springer Nature in BMC Medical Imaging
Open AccessWavelet-refined semantic-guided network for retinal vessel segmentation
- Open Access
- 15 June 2026
- Published by MDPI AG in Electronics
- Vol. 15 (12) , 2638
RKF-YOLO: A Lightweight Dual-Task Model for Illegal Parking Detection and License Plate Recognition on Edge Devices
To address the joint requirements of illegal parking detection and license plate recognition under complex traffic scenarios and limited edge-device resources, this study proposes RKF-YOLO, a lightweight dual-task model based on improved YOLOv11n that integrates Rep-CSP structural optimization, knowledge-transfer-enhanced training (KTET), and Focal-CIoU loss. Compared with YOLOv11n, RKF-YOLO reduces parameters and FLOPs by 38.2% and 38.1%, respectively, while improving and by 0.6 and 1.1 percentage points for parking detection; for plate detection, Focal-CIoU improves by 1.3 percentage points and contributes to a recognition accuracy of 95.7%. The unified framework uses a shared backbone and task-oriented detection heads to support vehicle-level illegal parking detection and license-plate-oriented localization. Rep-CSP enhances multi-scale feature representation, asymmetric channel reduction with feature compensation reduces redundant computation, and KTET improves convergence through optimizer and learning-rate migration. Deployment on RK3588 achieves 59.5 FPS for parking detection and 95.1% recognition accuracy, demonstrating real-time performance and practical applicability on resource-constrained edge devices.- 15 June 2026
- Published by MDPI AG in Electronics
- Vol. 15 (12) , 2638
Open AccessRKF-YOLO: A Lightweight Dual-Task Model for Illegal Parking Detection and License Plate Recognition on Edge Devices
To address the joint requirements of illegal parking detection and license plate recognition under complex traffic scenarios and limited edge-device resources, this study proposes RKF-YOLO, a lightweight dual-task model based on improved YOLOv11n that integrates Rep-CSP structural optimization, knowledge-transfer-enhanced training (KTET), and Focal-CIoU loss. Compared with YOLOv11n, RKF-YOLO reduces parameters and FLOPs by 38.2% and 38.1%, respectively, while improving and by 0.6 and 1.1 percentage points for parking detection; for plate detection, Focal-CIoU improves by 1.3 percentage points and contributes to a recognition accuracy of 95.7%. The unified framework uses a shared backbone and task-oriented detection heads to support vehicle-level illegal parking detection and license-plate-oriented localization. Rep-CSP enhances multi-scale feature representation, asymmetric channel reduction with feature compensation reduces redundant computation, and KTET improves convergence through optimizer and learning-rate migration. Deployment on RK3588 achieves 59.5 FPS for parking detection and 95.1% recognition accuracy, demonstrating real-time performance and practical applicability on resource-constrained edge devices.