23,216 research outputs found
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Trip hazards are a significant contributor to accidents on construction and
manufacturing sites, where over a third of Australian workplace injuries occur
[1]. Current safety inspections are labour intensive and limited by human
fallibility,making automation of trip hazard detection appealing from both a
safety and economic perspective. Trip hazards present an interesting challenge
to modern learning techniques because they are defined as much by affordance as
by object type; for example wires on a table are not a trip hazard, but can be
if lying on the ground. To address these challenges, we conduct a comprehensive
investigation into the performance characteristics of 11 different colour and
depth fusion approaches, including 4 fusion and one non fusion approach; using
colour and two types of depth images. Trained and tested on over 600 labelled
trip hazards over 4 floors and 2000m in an active construction
site,this approach was able to differentiate between identical objects in
different physical configurations (see Figure 1). Outperforming a colour-only
detector, our multi-modal trip detector fuses colour and depth information to
achieve a 4% absolute improvement in F1-score. These investigative results and
the extensive publicly available dataset moves us one step closer to assistive
or fully automated safety inspection systems on construction sites.Comment: 9 Pages, 12 Figures, 2 Tables, Accepted to Robotics and Automation
Letters (RA-L
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
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