8,658 research outputs found
Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs
Understanding the 3D structure of a scene is of vital importance, when it
comes to developing fully autonomous robots. To this end, we present a novel
deep learning based framework that estimates depth, surface normals and surface
curvature by only using a single RGB image. To the best of our knowledge this
is the first work to estimate surface curvature from colour using a machine
learning approach. Additionally, we demonstrate that by tuning the network to
infer well designed features, such as surface curvature, we can achieve
improved performance at estimating depth and normals.This indicates that
network guidance is still a useful aspect of designing and training a neural
network. We run extensive experiments where the network is trained to infer
different tasks while the model capacity is kept constant resulting in
different feature maps based on the tasks at hand. We outperform the previous
state-of-the-art benchmarks which jointly estimate depths and surface normals
while predicting surface curvature in parallel
A deep learning approach towards railway safety risk assessment
Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks
Evaluating geospatial context information for travel mode detection
Detecting travel modes from global navigation satellite system (GNSS)
trajectories is essential for understanding individual travel behavior and a
prerequisite for achieving sustainable transport systems. While studies have
acknowledged the benefits of incorporating geospatial context information into
travel mode detection models, few have summarized context modeling approaches
and analyzed the significance of these context features, hindering the
development of an efficient model. Here, we identify context representations
from related work and propose an analytical pipeline to assess the contribution
of geospatial context information for travel mode detection based on a random
forest model and the SHapley Additive exPlanation (SHAP) method. Through
experiments on a large-scale GNSS tracking dataset, we report that features
describing relationships with infrastructure networks, such as the distance to
the railway or road network, significantly contribute to the model's
prediction. Moreover, features related to the geospatial point entities help
identify public transport travel, but most land-use and land-cover features
barely contribute to the task. We finally reveal that geospatial contexts have
distinct contributions in identifying different travel modes, providing
insights into selecting appropriate context information and modeling
approaches. The results from this study enhance our understanding of the
relationship between movement and geospatial context and guide the
implementation of effective and efficient transport mode detection models.Comment: updated Method and Discussion; accepted by Journal of Transport
Geograph
Deep Learning based Virtual Point Tracking for Real-Time Target-less Dynamic Displacement Measurement in Railway Applications
In the application of computer-vision based displacement measurement, an
optical target is usually required to prove the reference. In the case that the
optical target cannot be attached to the measuring objective, edge detection,
feature matching and template matching are the most common approaches in
target-less photogrammetry. However, their performance significantly relies on
parameter settings. This becomes problematic in dynamic scenes where
complicated background texture exists and varies over time. To tackle this
issue, we propose virtual point tracking for real-time target-less dynamic
displacement measurement, incorporating deep learning techniques and domain
knowledge. Our approach consists of three steps: 1) automatic calibration for
detection of region of interest; 2) virtual point detection for each video
frame using deep convolutional neural network; 3) domain-knowledge based rule
engine for point tracking in adjacent frames. The proposed approach can be
executed on an edge computer in a real-time manner (i.e. over 30 frames per
second). We demonstrate our approach for a railway application, where the
lateral displacement of the wheel on the rail is measured during operation. We
also implement an algorithm using template matching and line detection as the
baseline for comparison. The numerical experiments have been performed to
evaluate the performance and the latency of our approach in the harsh railway
environment with noisy and varying backgrounds
Analysis on predict model of railway passenger travel factors judgment with soft-computing methods
Purpose: With the development of the transportation, more traveling factors acting on the railway passengers change greatly with the passengers’ choice. With the help of the modern information computing technology, the factors were integrated to realize quantitative analyze according to the travel purpose and travel cost.
Design/methodology/approach: The detailed comparative study was implemented with comparing the two soft-computing methods: genetic algorithm, BP neural network. The two methods with different idea were also studied in this model to discuss the key parameter setting and its applicable range.
Findings: During the study, the data about the railway passengers is difficult to analyzed detailed because of the inaccurate information. There are still many factors to affect the choice of passengers.
Research limitations/implications: The model-designing thought and its computing procession were also certificated with programming and data illustration according to thorough analysis. The comparative analysis was also proved effective and applicable to predict the railway passengers’ travel choice through the empirical study with soft-computing supporting.
Practical implications: The techniques of predicting and parameters’ choice were conducted with algorithm-operation supporting.
Originality/value: The detail form comparative study in this paper could be provided for researchers and managers and be applied in the practice according the actual demand.Peer Reviewe
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