1,843 research outputs found
Detection of Runway and Obstacles using Electro-optical and Infrared Sensors before Landing
For safe aircraft operations, detection of runway incursions especially during landing and takeoff is essential. And it is important that such detection technique is capable of detecting the distant objects so that pilot has enough response time to take corrective action. This paper presents techniques to detect runway and runway incursions using electro-optical color camera and medium wave infrared sensor on-board the aircraft during approach for landing. The detection process consists of horizon detection to reduce runway search space in sensor image and then detect runway and obstacles. The information is then presented to the pilot to improve pilot situational awareness. The performance of the proposed techniques are evaluated in flight simulators with simulated images of electro-optical and infrared sensors on-board the aircraft during approach for landing at a distance of 3 nautical miles from runway threshold during day/night and in low visibility CAT II foggy conditions. Effectiveness of the techniques with statistics of runway detection, miss detection and false alarm for different case studies have been provided and discussed.Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.276
Exploiting Low-confidence Pseudo-labels for Source-free Object Detection
Source-free object detection (SFOD) aims to adapt a source-trained detector
to an unlabeled target domain without access to the labeled source data.
Current SFOD methods utilize a threshold-based pseudo-label approach in the
adaptation phase, which is typically limited to high-confidence pseudo-labels
and results in a loss of information. To address this issue, we propose a new
approach to take full advantage of pseudo-labels by introducing high and low
confidence thresholds. Specifically, the pseudo-labels with confidence scores
above the high threshold are used conventionally, while those between the low
and high thresholds are exploited using the Low-confidence Pseudo-labels
Utilization (LPU) module. The LPU module consists of Proposal Soft Training
(PST) and Local Spatial Contrastive Learning (LSCL). PST generates soft labels
of proposals for soft training, which can mitigate the label mismatch problem.
LSCL exploits the local spatial relationship of proposals to improve the
model's ability to differentiate between spatially adjacent proposals, thereby
optimizing representational features further. Combining the two components
overcomes the challenges faced by traditional methods in utilizing
low-confidence pseudo-labels. Extensive experiments on five cross-domain object
detection benchmarks demonstrate that our proposed method outperforms the
previous SFOD methods, achieving state-of-the-art performance
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Extracting information related to weather and visual conditions at a given
time and space is indispensable for scene awareness, which strongly impacts our
behaviours, from simply walking in a city to riding a bike, driving a car, or
autonomous drive-assistance. Despite the significance of this subject, it is
still not been fully addressed by the machine intelligence relying on deep
learning and computer vision to detect the multi-labels of weather and visual
conditions with a unified method that can be easily used for practice. What has
been achieved to-date is rather sectorial models that address limited number of
labels that do not cover the wide spectrum of weather and visual conditions.
Nonetheless, weather and visual conditions are often addressed individually. In
this paper, we introduce a novel framework to automatically extract this
information from street-level images relying on deep learning and computer
vision using a unified method without any pre-defined constraints in the
processed images. A pipeline of four deep Convolutional Neural Network (CNN)
models, so-called the WeatherNet, is trained, relying on residual learning
using ResNet50 architecture, to extract various weather and visual conditions
such as Dawn/dusk, day and night for time detection, and glare for lighting
conditions, and clear, rainy, snowy, and foggy for weather conditions. The
WeatherNet shows strong performance in extracting this information from
user-defined images or video streams that can be used not limited to:
autonomous vehicles and drive-assistance systems, tracking behaviours,
safety-related research, or even for better understanding cities through images
for policy-makers.Comment: 11 pages, 8 figure
Street Viewer: An Autonomous Vision Based Traffic Tracking System
The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
In unsupervised domain adaptation (UDA), a model trained on source data (e.g.synthetic) is adapted to target data (e.g. real-world) without access to targetannotation. Most previous UDA methods struggle with classes that have a similarvisual appearance on the target domain as no ground truth is available to learnthe slight appearance differences. To address this problem, we propose a MaskedImage Consistency (MIC) module to enhance UDA by learning spatial contextrelations of the target domain as additional clues for robust visualrecognition. MIC enforces the consistency between predictions of masked targetimages, where random patches are withheld, and pseudo-labels that are generatedbased on the complete image by an exponential moving average teacher. Tominimize the consistency loss, the network has to learn to infer thepredictions of the masked regions from their context. Due to its simple anduniversal concept, MIC can be integrated into various UDA methods acrossdifferent visual recognition tasks such as image classification, semanticsegmentation, and object detection. MIC significantly improves thestate-of-the-art performance across the different recognition tasks forsynthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA. Forinstance, MIC achieves an unprecedented UDA performance of 75.9 mIoU and 92.8%on GTA-to-Cityscapes and VisDA-2017, respectively, which corresponds to animprovement of +2.1 and +3.0 percent points over the previous state of the art.The implementation is available at https://github.com/lhoyer/MIC.<br
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
In unsupervised domain adaptation (UDA), a model trained on source data (e.g.
synthetic) is adapted to target data (e.g. real-world) without access to target
annotation. Most previous UDA methods struggle with classes that have a similar
visual appearance on the target domain as no ground truth is available to learn
the slight appearance differences. To address this problem, we propose a Masked
Image Consistency (MIC) module to enhance UDA by learning spatial context
relations of the target domain as additional clues for robust visual
recognition. MIC enforces the consistency between predictions of masked target
images, where random patches are withheld, and pseudo-labels that are generated
based on the complete image by an exponential moving average teacher. To
minimize the consistency loss, the network has to learn to infer the
predictions of the masked regions from their context. Due to its simple and
universal concept, MIC can be integrated into various UDA methods across
different visual recognition tasks such as image classification, semantic
segmentation, and object detection. MIC significantly improves the
state-of-the-art performance across the different recognition tasks for
synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA. For
instance, MIC achieves an unprecedented UDA performance of 75.9 mIoU and 92.8%
on GTA-to-Cityscapes and VisDA-2017, respectively, which corresponds to an
improvement of +2.1 and +3.0 percent points over the previous state of the art.
The implementation is available at https://github.com/lhoyer/MIC.Comment: CVPR 202
Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
Inland waterways are critical for freight movement, but limited means exist
for monitoring their performance and usage by freight-carrying vessels, e.g.,
barges. While methods to track vessels, e.g., tug and tow boats, are publicly
available through Automatic Identification Systems (AIS), ways to track freight
tonnages and commodity flows carried on barges along these critical marine
highways are non-existent, especially in real-time settings. This paper
develops a method to detect barge traffic on inland waterways using existing
traffic cameras with opportune viewing angles. Deep learning models,
specifically, You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD),
and EfficientDet are employed. The model detects the presence of vessels and/or
barges from video and performs a classification (no vessel or barge, vessel
without barge, vessel with barge, and barge). A dataset of 331 annotated images
was collected from five existing traffic cameras along the Mississippi and Ohio
Rivers for model development. YOLOv8 achieves an F1-score of 96%, outperforming
YOLOv5, SSD, and EfficientDet models with 86%, 79%, and 77% respectively.
Sensitivity analysis was carried out regarding weather conditions (fog and
rain) and location (Mississippi and Ohio rivers). A background subtraction
technique was used to normalize video images across the various locations for
the location sensitivity analysis. This model can be used to detect the
presence of barges along river segments, which can be used for anonymous bulk
commodity tracking and monitoring. Such data is valuable for long-range
transportation planning efforts carried out by public transportation agencies,
in addition to operational and maintenance planning conducted by federal
agencies such as the US Army Corp of Engineers
Spectral Unsupervised Domain Adaptation for Visual Recognition
Unsupervised domain adaptation (UDA) aims to learn a well-performed model in
an unlabeled target domain by leveraging labeled data from one or multiple
related source domains. It remains a great challenge due to 1) the lack of
annotations in the target domain and 2) the rich discrepancy between the
distributions of source and target data. We propose Spectral UDA (SUDA), an
efficient yet effective UDA technique that works in the spectral space and is
generic across different visual recognition tasks in detection, classification
and segmentation. SUDA addresses UDA challenges from two perspectives. First,
it mitigates inter-domain discrepancies by a spectrum transformer (ST) that
maps source and target images into spectral space and learns to enhance
domain-invariant spectra while suppressing domain-variant spectra
simultaneously. To this end, we design novel adversarial multi-head spectrum
attention that leverages contextual information to identify domain-variant and
domain-invariant spectra effectively. Second, it mitigates the lack of
annotations in target domain by introducing multi-view spectral learning which
aims to learn comprehensive yet confident target representations by maximizing
the mutual information among multiple ST augmentations capturing different
spectral views of each target sample. Extensive experiments over different
visual tasks (e.g., detection, classification and segmentation) show that SUDA
achieves superior accuracy and it is also complementary with state-of-the-art
UDA methods with consistent performance boosts but little extra computation
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