In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs), and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance between them. We analyzed a dataset composed of 896 images, recorded in videos by a DJI Spark Drone. The training set used 60% of the images, 20% for validation, and 20% for the tests. Tests were performed to detect vehicles in different configurations, and the best result was achieved using the YOLO Full-608, with a mean Average Precision(mAP) of 95.6%. The accuracy of the results encourages the development of systems capable of estimating the safe distance between vehicles in motion, allowing mainly to minimize the risk of accidents.

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This chapter intends to present the main techniques for detecting objects within images. In recent years there have been remarkable advances in areas such as machine learning and pattern recognition, both using convolutional neural networks (CNNs). It is mainly due to the increased parallel processing power provided by graphics processing units (GPUs). In this chapter, the reader will understand the details of the state-of-the-art algorithms for object detection in images, namely, faster region convolutional neural network (Faster RCNN), you only look once (YOLO), and single shot multibox detector (SSD). We will present the advantages and disadvantages of each technique from a series of comparative tests. For this, we will use metrics such as accuracy, training difficulty, and characteristics to implement the algorithms. In this chapter, we intend to contribute to a better understanding of the state of the art in machine learning and convolutional networks for solving problems involving computational vision and object detection.

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In this paper, state-of-the-art architectures of Convolutional Neural Networks (CNNs) are explained and compared concerning authorship classification of famous paintings. The chosen CNNs architectures were VGG-16, VGG-19, Residual Neural Networks (ResNet), and Xception. The used dataset is available on the website Kaggle, under the title “Best Artworks of All Time”. Weighted classes for each artist with more than 200 paintings present in the dataset were created to represent and classify each artist’s style. The performed experiments resulted in an accuracy of up to 95% for the Xception architecture with an average F1-score of 0.87, 92% of accuracy with an average F1-score of 0.83 for the ResNet in its 50-layer configuration, while both of the VGG architectures did not present satisfactory results for the same amount of epochs, achieving at most 60% of accuracy.