• Ms.ANVITHA D
• Dr. Shashidhar Kini K

This study focuses on the development of real-time object detection systems used to extend extended deep learning models such as Lyophor (once one time) and SSD (single-shot detector). This system was developed to recognize and classify several objects in a live video feed, providing immediate results with high accuracy. Key features such as speed, accuracy and scalability are achieved through efficient model architecture and GPU acceleration. Data records such as CoCo and Pascal VOCs are used to train and validate models. Workflows include data collection, preprocessing, model training, evaluation, and providing real-time inference.

Object Detection, Deep Learning, YOLO, SSD, Real-Time Detection, COCO Dataset, TensorFlow, OpenCV, Convolutional Neural Networks (CNNs)

"REALTIME OBJECT DETECTION SYSTEM USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.1167-1169, July-2025, Available :http://www.jetir.org/papers/JETIRGX06225.pdf

"REALTIME OBJECT DETECTION SYSTEM USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp1167-1169, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06225.pdf