15,924 research outputs found
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
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
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