Automatic large-scale data acquisition via crowdsourcing for crosswalk classification: A deep learning approach

2019 International Joint Conference on Neural Networks (IJCNN)

et al. 2019

Self Cite

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).

Section: Introductionmentioning

confidence: 99%

Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

Arruda

Paixão

2019 International Joint Conference on Neural Networks (IJCNN)

et al. 2019

Self Cite

“…The success of deep learning applications on autonomous driving and advanced driver assistance systems (ADAS) is unequivocal. For instance, DNNs have been used in scene semantic segmentation [2], traffic light detection [3], crosswalk classification [4], [5], traffic sign detection [6], pedestrian analysis [7], car heading direction estimation [8] and many other applications. In this work, we focus on the traffic sign detection problem.…”

Section: Introductionmentioning

confidence: 99%

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Tabelini

Paixão

2019 International Joint Conference on Neural Networks (IJCNN)

et al. 2019

Self Cite

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning.

“…Autonomous driving has been a very active research area in the past few years [1,2,3,4,5,6]. For an autonomous vehicle to be safe, it has to be aware of its surroundings, which includes detecting pedestrians, traffic signs, and traffic lights.…”

Section: Introductionmentioning

confidence: 99%

Deep traffic light detection by overlaying synthetic context on arbitrary natural images

Mello

Tabelini

et al. 2021

Computers & Graphics

Self Cite

Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model.