Real-Time Object Detection Using SSD MobileNet Model of Machine Learning

Anurag Gupta, Darshan Yadav, Akash Raj, Ayushman Pathak
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Abstract

This research paper focuses on the application of computer vision techniques using Python and OpenCV for image analysis and interpretation. The main objective is to develop a system capable of performing various tasks such as object detection, recognition, and image processing. The project employs a combination of traditional computer vision algorithms and deep learning models to achieve accurate and efficient results. The research paper begins with essential preprocessing steps, including image acquisition, resizing, and noise reduction. Feature extraction techniques are utilized to capture relevant information from images, followed by object detection using methods like Haar cascades or deep learning-based approaches such as YOLO. Object recognition is achieved through feature matching or deep learning-based classification models. Furthermore, image processing techniques, including image enhancement, segmentation, and filtering, are applied to improve image quality and extract meaningful information. The system is implemented using Python programming language, leveraging the powerful OpenCV library for various computer vision tasks.
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基于机器学习的SSD MobileNet模型的实时目标检测
本研究论文着重于使用Python和OpenCV进行图像分析和解释的计算机视觉技术的应用。主要目标是开发一个能够执行各种任务的系统,如物体检测、识别和图像处理。该项目将传统的计算机视觉算法与深度学习模型相结合,以实现准确高效的结果。研究论文从基本的预处理步骤开始,包括图像采集,调整大小和降噪。利用特征提取技术从图像中捕获相关信息,然后使用Haar级联或基于深度学习的方法(如YOLO)进行对象检测。目标识别是通过特征匹配或基于深度学习的分类模型来实现的。此外,图像处理技术,包括图像增强,分割和滤波,用于提高图像质量和提取有意义的信息。该系统使用Python编程语言实现,利用强大的OpenCV库实现各种计算机视觉任务。
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