A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

计算机将各种视觉媒体视为一组数值。由于这种方法，他们需要图像处理算法来检查图像内容。该项目比较了3种主要的图像处理算法：单发检测（SSD），基于更快区域的卷积神经网络（Faster R-CNN）和仅看一次（YOLO），以找出最快和最高效的三种算法。在此比较分析中，使用Microsoft COCO（上下文中的公共对象）数据集，评估了这三种算法的性能，并根据诸如准确性，精度和F1分数之类的参数分析了它们的优势和局限性。从分析结果可以得出结论，任何一种算法相对于其他两种算法的适用性在很大程度上取决于它们所应用的用例。