5,355 research outputs found
Adaptation of a Deep Learning Algorithm for Traffic Sign Detection
Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make them difficult to apply in real-time applications. One-stage detection algorithms like Single Shot Multibox (SSD) and You Only Look Once (YOLO) are designed to be faster, but their accuracy is lower compared with the two-stage detector methods. In this project, a traffic sign detection algorithm is presented, which is inspired mainly by the SSD algorithm and its variants. The number of layers and the number of scales for object detection were modified to obtain the best balance in accuracy and speed detection. Experimental tests of this method over a traffic sign dataset give results of 93.75% mAP versus 89.35% mAP obtained using standard SSD+MobileNet, the speed of detection is 0.0124 s per image on a GPU
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
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
Perceptual Generative Adversarial Networks for Small Object Detection
Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited to pay off the computational cost. In this
work, we address the small object detection problem by developing a single
architecture that internally lifts representations of small objects to
"super-resolved" ones, achieving similar characteristics as large objects and
thus more discriminative for detection. For this purpose, we propose a new
Perceptual Generative Adversarial Network (Perceptual GAN) model that improves
small object detection through narrowing representation difference of small
objects from the large ones. Specifically, its generator learns to transfer
perceived poor representations of the small objects to super-resolved ones that
are similar enough to real large objects to fool a competing discriminator.
Meanwhile its discriminator competes with the generator to identify the
generated representation and imposes an additional perceptual requirement -
generated representations of small objects must be beneficial for detection
purpose - on the generator. Extensive evaluations on the challenging
Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the
superiority of Perceptual GAN in detecting small objects, including traffic
signs and pedestrians, over well-established state-of-the-arts
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Although traffic sign detection has been studied for years and great progress
has been made with the rise of deep learning technique, there are still many
problems remaining to be addressed. For complicated real-world traffic scenes,
there are two main challenges. Firstly, traffic signs are usually small size
objects, which makes it more difficult to detect than large ones; Secondly, it
is hard to distinguish false targets which resemble real traffic signs in
complex street scenes without context information. To handle these problems, we
propose a novel end-to-end deep learning method for traffic sign detection in
complex environments. Our contributions are as follows: 1) We propose a
multi-resolution feature fusion network architecture which exploits densely
connected deconvolution layers with skip connections, and can learn more
effective features for the small size object; 2) We frame the traffic sign
detection as a spatial sequence classification and regression task, and propose
a vertical spatial sequence attention (VSSA) module to gain more context
information for better detection performance. To comprehensively evaluate the
proposed method, we do experiments on several traffic sign datasets as well as
the general object detection dataset and the results have shown the
effectiveness of our proposed method
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