543 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
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
In this work, a deep learning approach has been developed to carry out road
detection using only LIDAR data. Starting from an unstructured point cloud,
top-view images encoding several basic statistics such as mean elevation and
density are generated. By considering a top-view representation, road detection
is reduced to a single-scale problem that can be addressed with a simple and
fast fully convolutional neural network (FCN). The FCN is specifically designed
for the task of pixel-wise semantic segmentation by combining a large receptive
field with high-resolution feature maps. The proposed system achieved excellent
performance and it is among the top-performing algorithms on the KITTI road
benchmark. Its fast inference makes it particularly suitable for real-time
applications
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
Visual surveillance technology is an indispensable functional component of
advanced traffic management systems. It has been applied to perform traffic
supervision tasks, such as object detection, tracking and recognition. However,
adverse weather conditions, e.g., fog, haze and mist, pose severe challenges
for video-based transportation surveillance. To eliminate the influences of
adverse weather conditions, we propose a dual attention and dual
frequency-guided dehazing network (termed DADFNet) for real-time visibility
enhancement. It consists of a dual attention module (DAM) and a high-low
frequency-guided sub-net (HLFN) to jointly consider the attention and frequency
mapping to guide haze-free scene reconstruction. Extensive experiments on both
synthetic and real-world images demonstrate the superiority of DADFNet over
state-of-the-art methods in terms of visibility enhancement and improvement in
detection accuracy. Furthermore, DADFNet only takes ms to process a 1,920
* 1,080 image on the 2080 Ti GPU, making it highly efficient for deployment in
intelligent transportation systems.Comment: This paper is accepted by AAAI 2022 Workshop: AI for Transportatio
PASS: Panoramic Annular Semantic Segmentation
Pixel-wise semantic segmentation is capable of unifying most of driving scene perception tasks, and has enabled striking progress in the context of navigation assistance, where an entire surrounding sensing is vital. However, current mainstream semantic segmenters are predominantly benchmarked against datasets featuring narrow Field of View (FoV), and a large part of vision-based intelligent vehicles use only a forward-facing camera. In this paper, we propose a Panoramic Annular Semantic Segmentation (PASS) framework to perceive the whole surrounding based on a compact panoramic annular lens system and an online panorama unfolding process. To facilitate the training of PASS models, we leverage conventional FoV imaging datasets, bypassing the efforts entailed to create fully dense panoramic annotations. To consistently exploit the rich contextual cues in the unfolded panorama, we adapt our real-time ERF-PSPNet to predict semantically meaningful feature maps in different segments, and fuse them to fulfill panoramic scene parsing. The innovation lies in the network adaptation to enable smooth and seamless segmentation, combined with an extended set of heterogeneous data augmentations to attain robustness in panoramic imagery. A comprehensive variety of experiments demonstrates the effectiveness for real-world surrounding perception in a single PASS, while the adaptation proposal is exceptionally positive for state-of-the-art efficient networks.Ministerio de EconomĂa y CompetitividadComunidad de Madri
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