Assistant professor
CS&AI
SRM University AP
01
Computer Vision, Object detection, Image Processing, Optimization of Models, Edge Deployment
Industrial, Medical image Analysis and developing Edge based Prototypes
Mail: a.vinodkumar@sru.edu.in
PhD in Electronics and Communication Department From SRM University AP
Master in VLSI and Embedded Systems From JNTU Kakinada affiliated
bachelor in Electronics and Communication Department From JNTU Kakinada affiliated
PG
UG
V. K. Ancha, V. Gonuguntla, and R. Vaddi, “CSPGhost-YOLO: a lightweight and robust model for real-time mixed PCB defect detection system,” Measurement, p. 120521, Jan. 2026, doi: https://doi.org/10.1016/j.measurement.2026.120521. (Q1, IF:5.6) (2026)
A. Gorle, U. K. Varadi, K. Majeti, B. Karra, V. K. Ancha and R. Vaddi, "A Lightweight Deep learning model for Early Disease Detection of Rice plants," 2025 Third International Conference on Industry 4.0 Technology (I4Tech), Pune, India, 2025, pp. 1-5, doi: 10.1109/I4Tech64670.2025.11277764.
Ancha, V.K., Gonuguntla, V. & Vaddi, R. TRSBi-YOLO: Transformer based lightweight and high-performance model for PCB defects detection. Journal of Supercomputing 81, 1277 (2025). https://doi.org/10.1007/s11227-025-07771-0 (Q1, IF:2.7) (2025 metrics)
Ancha, V.K., Gonuguntla, V. & Vaddi, R. GSS-YOLO: an improved YOLOV5 prediction head with slim-neck for defect detection in printed circuit board assembly. Signal Image Video Processing 19, 915 (2025). https://doi.org/10.1007/s11760-025-04511-y (Q2, IF: 2.1) (Q2)
K. Bhaskar, V. K. Ancha, R. Maharajan and R. Vaddi, "A Light Weight YOLOv5 Model for Strawberry Disease Detection and Classification," 2025 International Conference on Electronics, AI and Computing (EAIC), Jalandhar, India, 2025, pp. 1-5, doi: 10.1109/EAIC66483.2025.11101433
V. K. Ancha, V. Gonuguntla and R. Vaddi, "TR-YOLO: An Enhanced Model for PCB Mixed Defect Detection and Classification," 2025 International Conference on Electronics, AI and Computing (EAIC), Jalandhar, India, 2025, pp. 1-6, doi: 10.1109/EAIC66483.2025.11101424
V.K. Ancha V. Gonuguntla and R. Vaddi, " GC-YOLO: A Compact and Efficient Model for PCB Defect Detection and Classification" 16th International IEEE Conference on Computing Communication and Networking Technologies 16th ICCCNT May 2025 IIT-Indore Indore, India, 2025
V.K. Ancha V. Gonuguntla and R. Vaddi, " ResNet-YOLO: An Enhanced Model for Real-Time Defect Detection and Classification in Assembled PCBs" 16th International IEEE Conference on Computing Communication and Networking Technologies 16th ICCCNT 2025 IIT-Indore Indore, India, May 2025
V. K. Ancha and R. Vaddi, "A Novel ROI-based Dataset for PCB Defects Detection and Classification," 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), VIT Vellore, India, 2024, pp. 1-5, doi: 10.1109/AIIoT58432.2024.10574777
V. Kumar Ancha, F. N. Sibai, V. Gonuguntla and R. Vaddi, "Utilizing YOLO Models for Real-World Scenarios: Assessing Novel Mixed Defect Detection Dataset in PCBs," in IEEE Access, vol. 12, pp. 100983-100990, 2024, doi: 10.1109/ACCESS.2024.3430329. (Q1, IF:3.4) (Q1)
A system for real-time and accurate detection and classification of manufacturing defects in printed circuit boards (PCBs) is disclosed. The system integrates advanced imaging techniques with lightweight deep learning–based algorithms to automatically identify and categorize defects from PCBs during the production process.
A system for real-time and accurate detection and classification of manufacturing defects in Assembled printed circuit boards (PCBAs) is disclosed. The system integrates advanced imaging techniques with lightweight deep learning–based algorithms to automatically identify and categorize defects from PCBAs during the production process.
A system and method for early and accurate diagnosis of cataracts is disclosed. The invention utilizes advanced image acquisition and lightweight deep learning–based analysis to detect subtle lens opacities and classify the severity of cataracts at an early stage. By processing ocular images through optimized algorithms, the system provides reliable and rapid diagnostic outputs, enabling timely clinical intervention.
Received Best paper Award from NIT Jalandhar
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Got first prize at a research symposium in the track of AI conducted by Vignan's Foundation for Science, Technology & Research (Deemed to be a university) Guntur, Andhra Pradesh
