Detection of objects in the image in streaming mode using YOLOv5 і Faster R-CNN

Abstract

The accuracy of the model can be one of the main indicators, on a basis of which it is possible to conclude about the suitability of the model for its practical operation. However, taking into account the specifics of the identified task, it is also worth paying attention to the speed of the model, since there is a need to process data in streaming mode. To investigate the possibilities of using machine learning in an applied problem, two groups of object recognition models considered: YOLOv5 and Faster R-CNN. The purpose of the study is to analyze the architectural solutions of the most common object detection models YOLOv5 and Faster R-CNN to build a model to improve the speed and accuracy of object detection in an applied task or further combine them. A total of 550 training images and 105 validation images collected. A dataset of 573 images from the new location also collected for final validation of the models. The use of Roboflow provided for image annotation, which allows not only to mark images, but also to export annotated data sets in various formats. Training and validation of the models carried out on the Google Colab platform. The platform uses the Python programming language and the PyTorch framework. The yolov5 and detecron2 libraries for YOLOv5 and Faster R-CNN, respectively, used for model training and validation. To determine whether the result belongs to one of the four groups, the IOU metric is used, which is the ratio of the intersection area to the area of the union of the correct and predicted bounding frames. The size of the trained YOLOv5 and Faster R-CNN models was 40.2 MB and 230.8 MB, respectively. The models tested on the second validation set. As result of the study, a set of data from video surveillance cameras collected and anno-tated using RoboFlow. The main representatives of two groups of object detection algorithms YOLOv5 and Faster R-CNN trained using the prepared data set. The results showed that both models have their advantages and disadvantages, both models are applicable for different tasks.