IMMEDIATE OBJECT SPOTTING

Object Spotting has become one of the most essential and challenging tasks in computer vision, particularly in the context of deep learning advancements. This paper reviews the evolution of object detection techniques, highlighting how deep neural networks have transformed the field. We classify the state-of-the-art detection algorithms into three main categories: anchor-based, anchor-free, and transformer-based detectors, each with distinct methodologies for identifying objects in images. This survey includes a comparison of key convolutional neural network (CNN) architectures used for object detection, evaluating their speed-accuracy trade-offs, quality metrics, and training strategies. We discuss the strengths and limitations of these models and offer insights into the future directions of research in object spotting. In parallel, immediate object spotting using OpenCV has gained prominence for its ability to identify and localize objects within live video streams, particularly by leveraging pre-trained models like YOLOv3. This approach processes each video frame in real time, with preprocessing steps such as resizing and normalization to ensure compatibility with the deep learning model. YOLOv3’s efficiency enables it to detect and classify objects quickly, drawing bounding boxes around detected objects and displaying them in real time. This project highlights how combining OpenCV with deep learning models can achieve effective, scalable object spotting while ensuring detection accuracy. The paper also explores the challenges and potential future advancements in improving detection accuracy, robustness, and compatibility with modern hardware systems for diverse applications. Keywords:Immediate object spotting, OpenCV, YOLOv3, Deep learning models, Live video stream, Pre- processing (resizing, normalization), Object identification and localization, Bounding boxes, Detection accuracy, System compatibility