Comparison Of Object Detection Algorithms, There are many common libraries or application program interfaces (APIs) to use. With the gradual increase in the evolution of deep learning algorithms for detecting objects, a significant YOLOv11's outstanding performance in accuracy and speed solidifies its position as the most effective model for surface defect detection on the NEU dataset, surpassing competing The recent advancement in deep learning approaches of machine learning and computer vision technology has paved the way for many advancements in object detection prediction models used in Moving object detection and tracking from video sequences are a relevant research field since it can be used in many applications. With the emerging of numerous object detection framewo. These include YOLOv5, YOLOv6, and YOLOv7. This paper In this paper, six state-of-the-art object detection algorithms are presented, analysed and compared computationally using four diferent datasets, two single class and two multiple class datasets. YOLO models. This paper explores three representative series of methods Object detection, whose main task is to detect objects in a picture to determine the type, location, and scene to which they belong, has become one of the most Single-stage detectors streamline the detection process, making them faster, while two-stage detectors, although more complex, achieve higher accuracy. Additionally, comparative A technical guide to leading object detection algorithms for computer vision, covering two-stage, one-stage, and transformer-based algorithm A direct comparison between the most common object detection methods help in finding the best solution for advance system integration. In this Object detection is one of the most important and challenging branches of computer vision, whose main task is to classify and localize objects in images or videos. Over the past, it has gained much attention to do more research on computer Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them Two-stage object detection algorithms offer a powerful framework for accurate and robust object detection, and ongoing research aims to refine and improve these algorithms, expanding the Object detection works by matching features from the test subject to the features extracted from the training data.

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