A New Dataset and Performance Evaluation of a Region-based CNN for Urban Object Detection

In the last years, we have seen a large growth in the number of applications which use deep learning-based object detectors. Autonomous Driving Assistance Systems (ADAS) is one of the areas where it has more impact. In this work, we present a novel study that evaluates a state-of-the-art technique for urban object localization. In particular, we investigate the performance of the Faster R-CNN method to detect and localize urban objects in a variety of outdoor urban videos involving pedestrians, cars, bicycles and other objects moving in the scene. We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted in a vehicle. Besides, some of the data is weakly annotated so it can be used for testing weakly-supervised learning techniques. We have carried out extensive experiments demonstrating the effectiveness of the baseline approach, which achieved a 74.2% accuracy on the proposed dataset. Moreover, we have evaluated a baseline approach for traffic sign recognition achieving an accuracy of 98.1%. A ResNet-based architecture was trained and used for this purpose as a second stage of our object detector. The full dataset is available for download at http://www.rovit.ua.es/dataset/traffic/.