Abstract
Abstract: Object detection is a crucial task in computer vision with various practical applications, including surveillance, autonomous vehicles, and robotics. The YOLO (You Only Look Once) algorithm is a popular real-time object detection algorithm that has gained significant attention due to its high accuracy and speed. This algorithm processes the entire image at once and predicts bounding boxes and class probabilities for identified objects, making it ideal for time-sensitive applications. YOLO has evolved through various versions, with YOLOv5 being the latest and most advanced version that employs a feature pyramid network (FPN) and anchor boxes to improve its object detection accuracy. In this project, we aim to implement YOLOv5 for real-time object detection and image detection tasks. We will train the model on a suitable dataset and evaluate its performance on various benchmarks, comparing it with other advanced object detection algorithms. The project's outcome will provide a robust and efficient solution for real-time object detection that can aid quick decision-making in identifying object categories and their respective positions. It has practical applications in surveillance, automated driving, and robotics.
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