Ordered Minimum Distance Bag-of-Words Approach for Aerial Object Identification

Detecting potential aerial threats like drones with computer vision is at the paramount of interest for the protection of critical locations.This type of a system should prevent efficiently the false alarms caused by non-malign objects such as birds, which intrude the image plane. In this paper, we propose an improved version of a previously presented Speeded-up Robust Feature Transform (SURF) based algorithm, referred as Ordered Minimum Distance Bag-of-Words (omidBoW) to discriminate drones, birds and background from the patches, using an extended histogram set. We show that a SURF based object recognition can be well integrated to this context and this improved algorithm can increase accuracy up to 16% compared to regular bag-ofwords approach

Using Shape Descriptors for UAV Detection

The rapid development of Unmanned Aerial Vehicle (UAV) technology, -also known as drones- has raised concerns on the safety of critical locations such as governmental buildings, nuclear stations, crowded places etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. It has been reported numerous times that, one of the main challenges for aerial object recognition with computer vision is discriminating birds from the targets. In this work, we have used 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features and classified targets as a drone or bird by a neural network. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate

Generic Fourier Descriptors for Autonomous UAV Detection

With increasing number of Unmanned Aerial Vehicles (UAVs) -also known as drones- in our lives, safety and privacy concerns have arose. Especially, strategic locations such as governmental buildings, nuclear power stations etc. are under direct threat of these publicly available and easily accessible gadgets. Various methods are proposed as counter-measure, such as acoustics based detection, RF signal interception, micro-doppler RADAR etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. In this work, 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features (which are analyzed with a neural network) are used for classifying aerial targets as a drone or bird. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate

Laminar free-surface flow around emerging obstacles: Role of the obstacle elongation on the horseshoe vortex

International audienceAn emerging rectangular obstacle placed in a laminar boundary layer developing under a free-surface generates three vortical structures: a horseshoe vortex (HSV) in front of the obstacle, a wake downstream and two lateral recirculation zones at its sides. The present work investigates, through PIV measurements, the effect of the obstacle elongation (length over width ratio L/W) on the HSV, which is partly indirect through the modification of the two other vortical structures. Horizontal velocity fields in the near-bottom region show that an increase of the obstacle elongation leads to a higher adverse pressure gradient in front of the obstacle, and in consequence, to the longitudinal extension of the HSV. This modification of geometry, in turn, impacts the vortex dynamics of the HSV. On top of that, maps of spectra and oscillation direction obtained from velocity fields indicate that each of the three structures (HSV, wake and lateral recirculation zones) exhibits a proper oscillation frequency. As the oscillation associated to the wake is energetically dominant and is strong enough to travel upstream, it impacts the HSV dynamics for sufficiently short obstacles

Smoother than smooth: increasing the flow conveyance of an open-channel flow by using drag reduction methods

International audienceThe drag reduction method using polymer additives is a common strategy to minimize friction losses when carrying fluids (water, oil, or slurries) in pipes over long distances. Previous studies showed that the interactions between the polymer and turbulent structures of the flow tend to modify the streamwise velocity profile close to the walls by adding a so-called elastic sublayer between the classical viscous and log layers. The gain in linear head losses can reach up to 80% depending on the roughness of the walls and the concentration of polymers. The application of this technique to sewers and the subsequent gain in discharge capacity motivated this work to quantitatively measure the drag reduction in classical open-channel flows. Three measurement campaigns were performed in a dedicated long flume for several water discharges and several polymer concentrations: backwater curves over smooth and rough channel walls (including velocity and turbulent shear-stress profiles) and flows around emerging obstacles. The addition of polymers, even in limited concentrations, allowed a high friction decrease with the typical Darcy-Weisbach coefficient reduced by factors of 2 and 1.5, respectively, in smooth and rough walls configurations without obstacles, but without strong modifications of the nondimensional velocity profiles. In contrast, when adding emerging obstacles, the flow was unaffected by the inclusion of polymers, in agreement with the prediction of the literature. The drag reduction method by addition of small concentrations of polymers thus appears to be a promising technique to increase flow conveyance in open-channel flows

Towards unsupervised learning of speech features in the wild

International audienceRecent work on unsupervised contrastive learning of speech representation has shown promising results, but so far has mostly been applied to clean, curated speech datasets. Can it also be used with unprepared audio data "in the wild"? Here, we explore three potential problems in this setting: (i) presence of non-speech data, (ii) noisy or low quality speech data, and (iii) imbalance in speaker distribution. We show that on the Libri-light train set, which is itself a relatively clean speech-only dataset, these problems combined can already have a performance cost of up to 30% relative for the ABX score. We show that the first two problems can be alleviated by data filtering, with voice activity detection selecting speech segments, while perplexity of a model trained with clean data helping to discard entire files. We show that the third problem can be alleviated by learning a speaker embedding in the predictive branch of the model. We show that these techniques build more robust speech features that can be transferred to an ASR task in the low resource setting

Active and thermal imaging performance under bad weather conditions

Thermal imaging cameras are widely used in military contexts for their night vision capabilities and their observation range; there are based on passive infrared sensors (e.g. MWIR or LWIR range). Under bad weather conditions or when the target is partially hidden (e.g. foliage, military camouflage) they are more and more complemented by active imaging systems, a key technology to perform target identification at long range. The 2D flash imaging technique is based on a high powered pulsed laser source that illuminates the entire scene and a fast gated camera as the imaging system. Both technologies are well experienced under clear meteorological conditions; models including atmospheric effects such as turbulence are able to predict accurately their performances. However, under bad weather conditions such as rain, haze or snow, these models are not relevant. This paper introduces new models to predict performances under bad weather conditions for both active and infrared imaging systems. We first establish an enumeration of these “bad” atmospheric conditions, depending on their occurrence rate. Then we develop physical models to describe their intrinsic characteristics and their impact on the imaging system performances. Finally, we approximate these models to have a “first order” model easy to deploy for industrial applications. This theoretical work will be validated on real active and infrared data

Data Augmenting Contrastive Learning of Speech Representations in the Time Domain

Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech signal. However, it still under-performs other methods on unsupervised evaluation benchmarks. Here, we introduce WavAugment, a time-domain data augmentation library and find that applying augmentation in the past is generally more efficient and yields better performances than other methods. We find that a combination of pitch modification, additive noise and reverberation substantially increase the performance of CPC (relative improvement of 18-22%), beating the reference Libri-light results with 600 times less data. Using an out-of-domain dataset, time-domain data augmentation can push CPC to be on par with the state of the art on the Zero Speech Benchmark 2017. We also show that time-domain data augmentation consistently improves downstream limited-supervision phoneme classification tasks by a factor of 12-15% relative

Experiments and Models of Active and Thermal Imaging Under Bad Weather Conditions

Thermal imaging cameras are widely used in military contexts for their night vision capabilities and their observation range; there are based on passive infrared sensors (e.g. MWIR or LWIR range). Under bad weather conditions or when the target is partially hidden (e.g. foliage, military camouflage) they are more and more complemented by active imaging systems, a key technology to perform target identification at long range. The 2D flash imaging technique is based on a high powered pulsed laser source that illuminates the entire scene and a fast gated camera as the imaging system. Both technologies are well experienced under clear meteorological conditions; models including atmospheric effects such as turbulence are able to predict accurately their performances. However, under bad weather conditions such as rain, haze or snow, these models are not relevant. This paper introduces new models to predict performances under bad weather conditions for both active and infrared imaging systems. We point out their effects on controlled physical parameters (extinction, transmission, spatial resolution, thermal background, speckle, turbulence). Then we develop physical models to describe their intrinsic characteristics and their impact on the imaging system performances. Finally, we approximate these models to have a “first order” model easy to deploy for industrial applications. This theoretical work will be validated on real active and infrared data