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.

As humans, we do not have to strain ourselves when we interpret our surroundings through our visual senses. From the moment we begin to observe, we unconsciously train ourselves with the same set of images. Hence, distinguishing entities is not a difficult task for us. On the contrary, computer views all kinds of visual media as an array of numerical values. Due to this contrast in approach, they require image processing algorithms to examine the contents of images. This project presents a comparative analysis of 3 major image processing algorithms: SSD, Faster R-CNN, and YOLO. In this analysis, we have chosen the COCO dataset. With the help of the COCO dataset, we have evaluated the performance and accuracy of the three algorithms and analysed their strengths and weaknesses. Using the results obtained from our implementations, we determine the differences between how each algorithm runs and suitable applications for each. The parameters for evaluation are accuracy, precision, F1 score.