PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea

Condition assessment of bridge structures using statistical analysis of wavelets

La surveillance à distance des structures a émergé comme une préoccupation importante pour les ingénieurs afin de maintenir la sécurité et la fiabilité des infrastructures civiles pendant leur durée de vie. Les techniques de surveillance structurale (SHM) sont de plus en plus populaires pour fournir un diagnostic de "l'état" des structures en raison de leur vieillissement, de la dégradation des matériaux ou de défauts survenus pendant leur construction. Les limites de l'inspection visuelle et des techniques non destructives, qui sont couramment utilisées pour détecter des défauts extrêmes sur les parties accessibles des structures, ont conduit à la découverte de nouvelles technologies qui évaluent d’un seul tenant l'état global d'une structure surveillée. Les techniques de surveillance globale ont été largement utilisées pour la reconnaissance d'endommagement dans les grandes infrastructures civiles, telles que les ponts, sur la base d'une analyse modale de la réponse dynamique structurale. Cependant, en raison des caractéristiques complexes des structures oeuvrant sous des conditions environnementales variables et des incertitudes statistiques dans les paramètres modaux, les techniques de diagnostic actuelles n'ont pas été concluantes pour conduire à une méthodologie robuste et directe pour détecter les incréments de dommage avant qu'ils n'atteignent un stade critique. C’est ainsi que des techniques statistiques de reconnaissance de formes sont incorporées aux méthodes de détection d'endommagement basées sur les vibrations pour fournir une meilleure estimation de la probabilité de détection des dommages dans des applications in situ, ce qui est habituellement difficile compte tenu du rapport bruit à signal élevé. Néanmoins, cette partie du SHM est encore à son stade initial de développement et, par conséquent, d'autres tentatives sont nécessaires pour parvenir à une méthodologie fiable de détection de l'endommagement. Une stratégie de détection de dommages basée sur des aspects statistiques a été proposée pour détecter et localiser de faibles niveaux incrémentiels d'endommagement dans une poutre expérimentale pour laquelle tant le niveau d'endommagement que les conditions de retenue sont réglables (par exemple ancastrée-ancastrée et rotulée-rotulée). Premièrement, des expériences ont été effectuées dans des conditions de laboratoire contrôlées pour détecter de faibles niveaux d'endommagement induits (par exemple une fissure correspondant à 4% de la hauteur d’une section rectangulaire équivalente) simulant des scénarios d'endommagement de stade précoce pour des cas réels. Différents niveaux d'endommagement ont été simulés à deux endroits distincts le long de la poutre. Pour chaque série d'endommagement incrémentiel, des mesures répétées (~ 50 à 100) ont été effectuées pour tenir compte de l'incertitude et de la variabilité du premier mode de vibration de la structure en raison d'erreurs expérimentales et du bruit. Une technique d'analyse par ondelette basée sur les modes a été appliquée pour détecter les changements anormaux survenant dans les modes propres causées par le dommage. La réduction du bruit ainsi que les caractéristiques des agrégats ont été obtenues en mettant en œuvre l'analyse des composantes principales (PCA) pour l'ensemble des coefficients d'ondelettes calculés à des nœuds (ou positions) régulièrement espacés le long du mode propre. En rejetant les composantes qui contribuent le moins à la variance globale, les scores PCA correspondant aux premières composantes principales se sont révélés très corrélés avec de faibles niveaux d'endommagement incrémentiel. Des méthodes classiques d'essai d'hypothèses ont été effectuées sur les changements des paramètres de localisation des scores pour conclure objectivement et statistiquement, à un niveau de signification donné, sur la présence du dommage. Lorsqu'un dommage statistiquement significatif a été détecté, un nouvel algorithme basé sur les probabilités a été développé pour déterminer l'emplacement le plus probable de l'endommagement le long de la structure. Deuxièmement, se basant sur l'approche probabiliste, une série de tests a été effectuée dans une chambre environnementale à température contrôlée pour étudier les contributions relatives des effets de l’endommagement et de la température sur les propriétés dynamiques de la poutre afin d’estimer un facteur de correction pour l'ajustement des scores extraits. Il s'est avéré que la température avait un effet réversible sur la distribution des scores et que cet effet était plus grand lorsque le niveau d'endommagement était plus élevé. Les résultats obtenus pour les scores ajustés indiquent que la correction des effets réversibles de la température peut améliorer la probabilité de détection et minimiser les fausses alarmes. Les résultats expérimentaux indiquent que la contribution combinée des algorithmes utilisés dans cette étude était très efficace pour détecter de faibles niveaux d'endommagement incrémentiel à plusieurs endroits le long de la poutre tout en minimisant les effets indésirables du bruit et de la température dans les résultats. Les résultats de cette recherche démontrent que l'approche proposée est prometteuse pour la surveillance des structures. Cependant, une quantité importante de travail de validation est attendue avant sa mise en œuvre sur des structures réelles. Mots-clés : Détection et localisation des dommages, Poutre, Mode propre, Ondelette, Analyse des composantes principales, Rapport de probabilité, TempératureRemote monitoring of structures has emerged as an important concern for engineers to maintain safety and reliability of civil infrastructure during its service life. Structural Health Monitoring (SHM) techniques are increasingly becoming popular to provide ideas for diagnosis of the "state" of potential defects in structures due to aging, deterioration and fault during construction. The limitations of visual inspection and non-destructive techniques, which were commonly used to detect extreme defects on only accessible portions of structures, led to the discovery of new technologies which assess the "global state" of a monitored structure at once. Global monitoring techniques have been used extensively for the recognition of damage in large civil infrastructure, such as bridges, based on modal analysis of structural dynamic response. However, because of complicated features of real-life structures under varying environmental conditions and statistical uncertainties in modal parameters, current diagnosis techniques have not been conclusive in ascertaining a robust and straightforward methodology to detect damage increments before it reaches its critical stage. Statistical pattern recognition techniques are incorporated with vibration-based damage detection methods to provide a better estimate for the probability of the detection of damage in field applications, which is usually challenging given the high noise to signal ratio. Nevertheless, this part of SHM is still in its initial stage of development and, hence, further attempts are required to achieve a reliable damage detection methodology. A statistical-based damage detection strategy was proposed to detect and localize low levels of incremental damage in an experimental beam in which the level of damage and beam restraint conditions are adjustable (e.g. fixed-fixed and pinned-pinned). First, experiments were performed in controlled laboratory conditions to detect small levels of induced-damage (e.g. 4% crack height for an equivalent rectangular section) simulated for early stage damage scenarios in real cases. Various levels of damage were simulated at two distinct locations along the beam. For each sate of incremental damage, repeat measurements (~ 50 to 100) were performed to account for uncertainty and variability in the first vibration mode of the structure due to experimental errors and noise. A modal-based wavelet analysis technique was applied to detect abnormal changes occurring in the mode shapes caused by damage. Noise reduction as well as aggregate characteristics were obtained by implementing the Principal Component Analysis (PCA) into the set of wavelet coefficients computed at regularly spaced nodes along the mode shape. By discarding components that contribute least to the overall variance, the PCA scores corresponding to the first few PCs were found to be highly correlated with low levels of incremental damage. Classical hypothesis testing methods were performed on changes on the location parameters of the scores to conclude damage objectively and statistically at a given significance level. When a statistically significant damage was detected, a novel Likelihood-based algorithm was developed to determine the most likely location of damage along the structure. Secondly, given the likelihood approach, a series of tests were carried out in a climate-controlled room to investigate the relative contributions of damage and temperature effects on the dynamic properties of the beam and to estimate a correction factor for the adjustment of scores extracted. It was found that the temperature had a reversible effect on the distribution of scores and that the effect was larger when the damage level was higher. The resulted obtained for the adjusted scores indicated that the correction for reversible effects of temperature can improve the probability of detection and minimize false alarms. The experimental results indicate that the combined contribution of the algorithms used in this study were very efficient to detect small-scale levels of incremental damage at multiple locations along the beam, while minimizing undesired effects of noise and temperature in the results. The results of this research demonstrate that the proposed approach may be used as a promising tool for SHM of actual structures. However, a significant amount of challenging work is expected for implementing it on real structures. Key-words: Damage Detection and Localization, Beam, Mode Shape, Wavelet, Principal Component Analysis, Likelihood Ratio, Temperatur

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version