6,991 research outputs found
Traffic Sign Detection and Recognition Based on Convolutional Neural Network
As autonomous vehicles are developing and maturing the technology to implement the domestic autonomous vehicles. The critical technological problem for self-driving vehicles is traffic sign detection and recognition. A traffic sign recognition system is essential for an intelligent transportation system. The digital image processing techniques for object recognition and extraction of features from visual objects is a huge process and include many conversions and pre-processing steps. A deep learning-based convolutional neural network (CNN) model is one of the suitable approach for traffic sign detection and recognition. This model has overcome significant shortcomings of traditional visual object detection approaches. This paper proposed a traffic sign identification and detection system. The proposed design and strategy are implemented using the Tensorflow framework in google colab environment. The experiment is applied on the publicly available traffic sign data sets. The defined deep convolution neural network based model experimental results achieved 94.52% and 80.85% precision and recall respectively. Improving the seep of recognition and identifying appropriate features of traffic sign objects are addressed using deep learning-based encoders and transformers.
 
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity
Deep supervised learning using local errors
Error backpropagation is a highly effective mechanism for learning
high-quality hierarchical features in deep networks. Updating the features or
weights in one layer, however, requires waiting for the propagation of error
signals from higher layers. Learning using delayed and non-local errors makes
it hard to reconcile backpropagation with the learning mechanisms observed in
biological neural networks as it requires the neurons to maintain a memory of
the input long enough until the higher-layer errors arrive. In this paper, we
propose an alternative learning mechanism where errors are generated locally in
each layer using fixed, random auxiliary classifiers. Lower layers could thus
be trained independently of higher layers and training could either proceed
layer by layer, or simultaneously in all layers using local error information.
We address biological plausibility concerns such as weight symmetry
requirements and show that the proposed learning mechanism based on fixed,
broad, and random tuning of each neuron to the classification categories
outperforms the biologically-motivated feedback alignment learning technique on
the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard
backpropagation. Our approach highlights a potential biological mechanism for
the supervised, or task-dependent, learning of feature hierarchies. In
addition, we show that it is well suited for learning deep networks in custom
hardware where it can drastically reduce memory traffic and data communication
overheads
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
In recent years, data-driven methods have shown great success for extracting
information about the infrastructure in urban areas. These algorithms are
usually trained on large datasets consisting of thousands or millions of
labeled training examples. While large datasets have been published regarding
cars, for cyclists very few labeled data is available although appearance,
point of view, and positioning of even relevant objects differ. Unfortunately,
labeling data is costly and requires a huge amount of work. In this paper, we
thus address the problem of learning with very few labels. The aim is to
recognize particular traffic signs in crowdsourced data to collect information
which is of interest to cyclists. We propose a system for object recognition
that is trained with only 15 examples per class on average. To achieve this, we
combine the advantages of convolutional neural networks and random forests to
learn a patch-wise classifier. In the next step, we map the random forest to a
neural network and transform the classifier to a fully convolutional network.
Thereby, the processing of full images is significantly accelerated and
bounding boxes can be predicted. Finally, we integrate data of the Global
Positioning System (GPS) to localize the predictions on the map. In comparison
to Faster R-CNN and other networks for object recognition or algorithms for
transfer learning, we considerably reduce the required amount of labeled data.
We demonstrate good performance on the recognition of traffic signs for
cyclists as well as their localization in maps.Comment: Submitted to IV 2018. This research was supported by German Research
Foundation DFG within Priority Research Programme 1894 "Volunteered
Geographic Information: Interpretation, Visualization and Social Computing
Fast traffic sign recognition using color segmentation and deep convolutional networks
The use of Computer Vision techniques for the automatic
recognition of road signs is fundamental for the development of intelli-
gent vehicles and advanced driver assistance systems. In this paper, we
describe a procedure based on color segmentation, Histogram of Ori-
ented Gradients (HOG), and Convolutional Neural Networks (CNN) for
detecting and classifying road signs. Detection is speeded up by a pre-
processing step to reduce the search space, while classication is carried
out by using a Deep Learning technique. A quantitative evaluation of the
proposed approach has been conducted on the well-known German Traf-
c Sign data set and on the novel Data set of Italian Trac Signs (DITS),
which is publicly available and contains challenging sequences captured
in adverse weather conditions and in an urban scenario at night-time.
Experimental results demonstrate the eectiveness of the proposed ap-
proach in terms of both classication accuracy and computational speed
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