Detection Of Man-made Objects Research Articles

Compound Preselector for Multistatic Passive Radar Employed in Remote Sensing Applications

Passive radars and multistatic passive radars in particular are gaining increasing attention. It is due to their effectiveness in detecting, tracking and identifying targets. Multistatic passive radars are used in remote sensing observations of the Earth’s ionosphere, meteors, space junk, etc. Another application is the detection and tracking of manmade objects and vehicles in the atmosphere or on the surface of the Earth. The current paper discusses a hardware design approach applicable to the front-end of a multistatic passive radar receiver, namely a compound preselector. The preselector is a very important part of the radio receiver in cases where there are strong neighbouring signals in the radio band of operation. Such strong signals may overload the receiver or the low noise preamplifier and render the whole system dysfunctional. This article elaborates on a two-component preselector having very high rejection ratio to nearby emitters, still keeping the receiver simple and reliable. The authors have registered several utility models on innovations in multistatic passive radars at Patent Office of Republic of Bulgaria.

Detection and classification of man-made objects for the autonomy of underwater robots

Recent developments in marine technologies allow underwater vehicles to perform survey missions for data collection in an automatic way. The scientific community is now focusing on endowing these vehicles with strong perception capabilities, aiming at full autonomy and decision-making skills. Such abilities would bring benefits to a wide range of field applications, e.g. Inspection and Maintenance (I&M) of man-made structures, port security, and marine rescue. Indeed, most of these tasks are currently carried out employing remotely operated vehicles, making the presence of humans in water necessary. Projects like Metrological Evaluation and Testing of Robots in International CompetitionS (METRICS), funded by the European Commission, are promoting research on this field by organising events such as the Robotics for Asset Maintenance and Inspection (RAMI) competition. In particular, this competition requires participants to develop perception techniques capable of identifying a set of specific targets. Within such context, this paper presents an algorithm able to detect and classify Objects of Potential Interest (OPIs) in underwater camera images. First, the proposed solution compensates for the quality degradation of underwater images by applying color enhancement and restoration procedures. Then, it exploits deep-learning techniques, as well as color and shape based methods, to recognize and correctly label the predefined OPIs. Preliminary results of the implemented neural network using restored images are provided, and a mean Average Precision (mAP) of about 92% was achieved on the dataset provided to the RAMI competition participating teams by the NATO Science and Technology Organization Centre for Maritime Research and Experimentation (STO CMRE).

Investigating of the impact of biommetic pinna dynamics on sonar tasks in natural environments

Bats are known to perform incredible feats using their echolocation abilities, which include natural tasks, such as navigation through foliage and hunting prey, as well as tasks they have been trained to perform, such as classification of man-made objects. Much work has been done to engineer biomimetic systems that can perform these and similar tasks in a comparable way to bats. Biomimetic sonar devices have performed tasks such as identifying a geographic location, object classification, and foliage gap finding. Particular species of bats (notably those of the families Hipposideridae and Rhinolophidae) use rapid ear motion while making sonar calls, and the full effect and purpose of this motion is not fully understood. Our research goal is to perform sonar tasks with a biomimetic sonar head capable of both static and dynamic ear motion and investigate the effect of dynamic ear motion on captured echoes and performance on tasks. To this end, we have collected echo data in natural habitats with dense tropical vegetation in Brunei on the island of Borneo. Tasks currently under investigation include location identification and detection of man-made objects in foliage, and are performed by deep learning classifiers trained on echoes taken by the biomimetic sonar device.

Validation of Targets in Sonar Imagery Using Multispectral Analysis

The detection of underwater objects in sonar imagery is a key enabling technique, for applications ranging from mine hunting and seabed characterization to marine archaeology. Owing to the nonhomogeneity of the sonar imagery, the majority of detection approaches are geared toward the detection of features in the spatial domain to identify anomalies in the seabed’s background. Yet, when the seabed is complex and includes rocks and sand ripples, spatial features are hard to discriminate, leading to high false alarm rates. With the aim of validating the detection of man-made objects in complex environments, we utilize the expected spectral diversity of reflections. This way, we can differentiate man-made objects’ reflections from the relatively flat frequency response of natural objects’ reflections, such as rocks. Our solution merges a set of preregistered sonar images of the same scene that are obtained at a different center frequency. For low- or high-resolution sonar images, we apply the Jain’s fairness index or the Kullback–Leibler divergence, respectively, to evaluate the spectral diversity of the reflections of a given region of interest and, thus, detect anomalies across the spectrum domain. We test our algorithm over simulated data and over images collected in three designated sea experiments: a data set that we share with the community. The results show that, compared with benchmark schemes, our solution achieves lower false alarm rates while preserving high detection level.

AS-polRI: Analysis of apparent spectral polarization radiant intensity in the midwave infrared band for man-made object detection

Detecting remote man-made objects in mid-infrared band is an important problem in aerial surveillance. Spectral polarization in the midwave infrared (MWIR) hyperspectral band can be a feasible solution but it has been studied less than the visible light band. The conventional degree of linear polarization (DoLP) metric cannot provide physical spectral polarization information because it is defined as a energy-normalized ratio, which leads to incorrect polarization degree in low signal-to-noise ratio in MWIR band. This paper presents a novel physical metric called apparent spectral polarization radiant intensity (AS-polRI) for MWIR spectral polarization in man-made object detection. AS-polRI, measured in Watts per steradian (W/sr), is obtained by integrating the difference between parallel spectral radiance and perpendicular spectral radiance assuming a 1 m2 object area. The proposed AS-polRI is compared with the degree of linear polarization (DoLP) in terms of man-made object detection. Experimental results for various outdoor scenarios and materials validate the usefulness of the proposed AS-polRI.

Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images

Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

Uydu Görüntülerinden Çok Ölçekli Uçak Tespiti

Abstract Satellite image analysis is a research area in which many research studies are carried out for civil and military applications in the field of image processing. Satellite imagery has many applications including recognition, detection and classification of regions, buildings, roads, aircraft and other man-made objects. Among these, especially aircraft detection is strategically important for military applications, and forms the basis of this study. In the first phase of the study, a new dataset of aircrafts is created from Google Earth images to compensate the shortage of data set in this area. In the second stage, the detection of air vehicles was carried out using algorithms based on Convolutional Neural Network (CNN). Region-based Fully Convolutional Network (R-FCN), Single Shot Multi Box Detector (SSD) and Faster R-CNN methods are used for this process. The obtained accuracy rate for R-FCN, SSD and Faster R-CNN are 98.01%, 69.71% and 96.56%, respectively.

Експериментальні дослідження процесу виявлення нелінійних переходів вибухових пристроїв з неконтактними датчиками цілі

Кардинальні зміни характеру та значне збільшення динаміки ведення мінної війни у воєнних конфліктах сучасності стали результатом зміщення пріоритетів у бік застосування новітніх зразків мінної зброї та саморобних вибухових пристроїв переважно з неконтактними датчиками цілі. Такий стан питання викликав загострення проблеми протидії вибухонебезпечним загрозам, а поряд з цим потребу у забезпеченні потрібного рівня ефективності, безпеки та собівартості процесів пошуку і виявлення вибухових пристроїв з неконтактними (електронними) датчиками цілі. Існуючі технічні засоби пошуку та виявлення вибухонебезпечних предметів, як правило, у більшості випадків ґрунтуються на використанні активних електромагнітних методів. Наближення таких пошукових пристроїв до вибухових пристроїв із магнітними (неконтактними) датчиками цілі або тих, що керуються по радіо, досить часто призводить до несанкціонованого вибуху і як наслідок до втрат.
 Встановлено, що майже усі неконтактні датчики цілі поєднує спільна ознака – наявність напівпровідникових елементів у їхньому складі, або переходів типу метал-окисел-метал. Вказана особливість покладена в основу сучасних засобів виявлення вибухових пристроїв з неконтактними датчиками цілі методом нелінійної радіолокації. Однак, при цьому проблемними досі залишаються вимоги щодо безпеки використання пошукових пристроїв даного типу у ручному варіанті або на наземних транспортних засобах. Одним із шляхів розв’язання даного протиріччя є встановлення пошукових пристроїв на дистанційно-керовану платформу. При чому, виникає нагальна потреба обґрунтування тактико-технічних вимог до засобів даного типу при зміні умов та способів їх застосування.
 На основі проведеного аналізу попередніх досліджень, як вітчизняних, так і закордонних фахівців, стосовно використання електромагнітних методів пошуку і виявлення рукотворних предметів в укриваючих середовищах (ґрунт, сніг, вода тощо), запропоновано науково-методичний підхід щодо планування експериментальних досліджень процесу виявлення нелінійних переходів вибухових пристроїв з неконтактними датчиками цілі. Експериментальні дослідження, призначені для перевірки і уточнення розроблених теоретичних положень щодо створення перспективних дистанційно керованих радіолокаційних комплексів пошуку та виявлення вибухових пристроїв з неконтактними датчиками цілі за допомогою методу нелінійної радіолокації, а у випадку їх підтвердження – технічного обґрунтування вимог до пошукових пристроїв нелінійної радіолокації та дистанційно керованих радіолокаційних комплексів в цілому.

МАТЕМАТИЧНА МОДЕЛЬ ПОШУКУ ТА ВИЯВЛЕННЯ ВИБУХОВИХ ПРИСТРОЇВ З НЕКОНТАКТНИМИ ДАТЧИКАМИ ЦІЛІ МЕТОДОМ НЕЛІНІЙНОЇ РАДІОЛОКАЦІЇ

Radical changes in the nature and significant increase in the dynamics of mine warfare in modern military conflicts are the result of shifting priorities towards the use of the latest models of minesweepers and improvised explosive devices with mostly non-contact target sensors. This state of affairs has exacerbated the problem of countering explosive threats, and with it the need to ensure the required level of efficiency, safety and cost of search and detection of explosive devices with non-contact (electronic) target sensors. Existing technical means of search and detection of explosive objects, as a rule, in most cases are based on the use of active electromagnetic methods. The approach of such search devices to explosive devices with magnetic (non-contact) target sensors or those controlled by radio, often led to an unauthorized explosion and as a consequence to losses among the sappers.It is established that almost all non-contact sensors of the target are united by a common feature - the presence of semiconductor elements in their composition, or transitions such as metal-oxide-metal. These features are the basis of modern means of detecting explosive devices with non-contact target sensors by nonlinear radar. However, the requirements for the safety of using search engines of this type in the manual version or on land vehicles are still problematic. One way to resolve this contradiction is to install search engines on a remote-controlled platform. Moreover, there is an urgent need to substantiate the tactical and technical requirements for this type of equipment when changing the conditions and methods of their application.Based on the analysis of previous studies, both domestic and foreign experts, on the use of electromagnetic methods of search and detection of man-made objects in sheltered environments (soil, snow, water, etc.), proposed an improved mathematical model of search and detection of explosive devices with non-contact target sensors nonlinear radar. This model, in contrast to the existing ones, takes into account the probabilistic nature of the search process and the plane indicators of the guaranteed detection zone of devices with non-contact target sensors by nonlinear radar, functional indicators of electromagnetic wave propagation in free space and shelter. In the field of nonlinear radar, the concept of "zone of guaranteed detection of explosive devices with non-contact target sensors" was introduced for the first time, the essence of which is to ensure the implementation of the requirements for equivalent probability of detection at a level not lower than specified. In addition, the proposed mathematical model, among other technical characteristics, allows to set the performance parameters of the remote-controlled radar complex search and detection of explosive devices with non-contact target sensors by nonlinear radar. The proposed improvements of the mathematical model and the sequence of calculations will increase the accuracy of the results of modeling the search and detection of explosive devices with non-contact target sensors in sheltered environments when substantiating the requirements for demining of this type.

Detection of Man-made objects along the High-speed Railway Based on High Resolution Remote Sensing Images

The condition along the high-speed railway is very important for the safe operation. In the paper, man-made objects along the high-speed railway are used as detecting targets. The paper analyses the existing forms of hidden dangers which are caused by man-made objects along the high-speed railway and what harms they could bring to operation of the high-speed railway. The method of detecting man-made objects along the high-speed railway based on high resolution remote sensing images is studied using remote sensing image classification. According to the established method which is suitable for detecting man-made objects along the high-speed railway, firstly detecting and extracting man-made objects along the high-speed railway, and improving classification accuracy by GIS vector data. Then, using the method to detect man-made objects along the Xuzhou section of Beijing-Shanghai high-speed railway based on GF-3. The results show that there is hidden danger of the plastic house, which proves the effectiveness and practical significance of the method.

Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Conducting surveillance of the geocenric orbits is a key task towards achieving space situational awareness (SSA). Our work focuses on the optical detection of man-made objects (e.g., satellites, space debris) in Geostationary orbit (GEO), which is home to major space assets such as telecommunications and Earth observing satellites. GEO object detection is challenging due to the distance of the targets, which appear as small dim point-like objects among a background of streak-like objects. In this paper, we propose a novel multi-target detection technique based on topological sweep, to find GEO objects from a short sequence of optical images. Our topological sweep technique exploits the geometric duality that underpins the approximately linear trajectory of target objects across the sequence, to extract the targets from significant clutter and noise. Unlike standard multi-target methods, our algorithm deterministically solves a combinatorial problem to ensure high-recall rates without requiring accurate initializations. The usage of geometric duality also yields an algorithm that is computationally efficient and suitable for online processing.

Salient man-made object detection based on saliency potential energy for unmanned aerial vehicles remote sensing image

It is difficult to automatically recognize complex ground objects, and massive data images with the super-high ground resolution in images captured by unmanned aerial vehicles (UAVs). In order to directly identify the salient man-made ground objects from the UAV remote sensing (RS) image, a saliency detection method based on saliency potential energy (SPE) is proposed. With a detection, filtration and backtracking strategy, the texture, shape and colour of the UAV RS image are comprehensively and numerally analysed by the SPE to detect the salient man-made objects. Both qualitative and quantitative evaluations have indicated that, compared to the state-of-art saliency detection methods, our method could achieve better performance with better accuracy and less errors, which prove that our method has great application potential in UAV RS image understanding.

Unsupervised seabed characterization with Bayesian modeling of SAS imagery

Seabed characterization has utility for numerous applications that seek to explore and interact with the seafloor, ranging from costal habitat monitoring and sub-bottom profiling to man-made object detection. In the work presented here, we characterize the seabed based on the texture patterns within SAS images constructed from high-frequency side-scan sonar. Features are measured from the SAS images (e.g., lacunarity, an established texture feature coding method, and a rotationally invariant histogram of oriented gradients). Based on these SAS image features, we perform unsupervised clustering with a hierarchical Bayesian model, which creates categories of seabed textures. Our clustering model is a new variant of the hierarchical Dirichlet process that is both adaptive to changes in the seabed and processes batches of SAS imagery in an online fashion. This allows the model to learn new seabed types as they are encountered and provides usable clustering results after each batch is processed, rather than having to wait for all the data to be collected before being processed. The model’s performance of seabed characterization by SAS image texture is demonstrated in the overall range and internal consistency of textures specific to each learned cluster. [Work supported by the Office of Naval Research.]

A man-made object detection algorithm based on contour complexity evaluation

Man-made object detection is of great significance in both military and civil areas, such as search-and-rescue missions at sea, traffic signs recognition during visual navigation, and targets location in a military strike. Contours of man-made objects usually consist of straight lines, corner points, and simple curves. Motivated by this observation, a man-made object detection method is proposed based on complexity evaluation of object contours. After salient contours which keep the crucial information of objects are accurately extracted using an improved mean-shift clustering algorithm, a novel approach is presented to evaluate the complexity of contours. By comparing the entropy values of contours before/after sampling and linear interpolation, it is easy to distinguish between man-made objects and natural ones according to the complexity of their contours. Experimental results show that the presented method can effectively detect man-made objects when compared to the existing ones.

Underwater Man-Made Object Recognition on the Basis of Color and Shape Features

ABSTRACT Hou, G.-J.; Luan, X.; Song, D.-L., and Ma, X.-Y., 2016. Underwater mad-made object recognition on the basis of color and shape features. In complex underwater situations, how to realize object extraction accurately and effectively is the key technology of underwater object recognition. In this paper, the detection and recognition techniques of underwater man-made objects on the basis of color and shape features have been studied in depth. First, the objects of interest in an underwater image are extracted by applying a color-based algorithm. Then an improved two-dimensional Otsu algorithm is utilized for removing the background color noise. To recognize the shape type of a regular object, a robust algorithm based on shape signature is presented. The experimental results show that the proposed approach is effective and robust, such as an acceptable extraction rate (exceeding 80%) of the object of interest, an ideal outcome of background color noise removal, high accurate shape of the object's edge...

Plane surface detection and reconstruction using segment-based tensor voting

A Segment-based Tensor Voting (SBTV) algorithm is presented for planar surface detection and reconstruction of man-made objects. Our work is inspired by piecewise planar stereo reconstruction. During the vital procedure to detect and label the planar surface, the two main contributions are: first, tensor voting is used for obtaining the geometry attribute of the 3D points cloud. The candidate planar patches are generated through scene image segment of low variation of color and intensity. Second, we over-segment the scene image into the segment and the candidate 3D planar patch is generated. The SBTV algorithm is used on 3D points cloud sets to identify the co-plane on the candidate patch. After detecting every planar patch, the geometry architecture of object is obtained. The experiments demonstrate the effectiveness of our proposed approach on either outdoor or indoor datasets.

Underwater manmade and archaeological object detection in optical and acoustic data

We propose a method designed for processing acoustic and optical data producing information about the presence of manmade and archaeological objects lying on the seabed. This method statistically highlights this type of artifacts among surrounding environment, weighting properly the persistence of meaningful curves in a video sequence, or in a sonogram. To this aim, we made use of the ELSD algorithm, a parameterless method inspired by Gestalt principles which has proven to provide promising results.

Identifying Man-Made Objects Along Urban Road Corridors From Mobile LiDAR Data

This letter is dedicated to a generic approach for the automated detection and classification of man-made objects in urban corridors from point clouds acquired by vehicle-borne mobile laser scanning (MLS). The approach is designed based on a priori knowledge in urban areas: 1) man-made objects feature geometric regularity such as vertical planar structures (e.g., building facades), whereas vegetation reveals huge diversity in shape and point distribution and 2) different types of urban man-made objects can be characterized by the point extension and the height above the ground level. Therefore, MLS-based point clouds are first divided into three layers with respect to the vertical height. In each layer, seed points of man-made objects are indicated by a line filter in the footprints of off-ground objects, which is generated by binarizing the spatial accumulation map of the point clouds. These seed points are further classified by examining in which layers the seed points of objects are found. Finally, points belonging to respective objects can be retrieved based on the classified seed points. The experiments show that various man-made objects on both sides of the street can be well detected, with a detection rate of up to 83%. For the classification of detected urban objects, overall accuracy of 92.37% can be achieved.

Improving Automatic Target Recognition with Forward Looking Sonar Mosaics

Automatic Target Recognition (ATR) is a key element needed to make Mine Countermeasure missions using robots entirely autonomous. While there has been much progress in applying ATR algorithms on high-resolution Synthetic Aperture Sonar (SAS) and sidescan sonar data, performing ATR with a low cost Forward Looking Sonar (FLS) is much more challenging. An algorithm for the detection of underwater man-made objects in FLS previously developed can work in real-time although it suffers considerably from typical noise in sonar images and false alarms. The work presented here shows that ATR algorithms can be exercised on sonar mosaics built also in real-time instead of raw data coming from the FLS. The use of mosaics can help the detection of the targets by reducing some noise (including harmonics from other acoustic devices mounted on the robot) and giving a better contrast to the images to be processed. Moreover, mosaic images can be useful for post-processing and data analysis. The mosaicking algorithm also runs in real-time to maintain the performance of the system and to be useful in real missions. It was tested both on data previously collected and in real experiments with different set-ups and with different sonars. The wide range of results obtained with different surface vehicles and in different situations demonstrate the usefulness of the method.

Detection of anthropogenic zones by means of spatial anomaly detection in large-scale satellite images

A comparative study of the informativeness of statistical and structural features in anthropogenic zone detection in high-resolution satellite images by using decision rules formed on the basis of the Bayes and Neumann-Pearson criteria is performed. It is found that the greatest efficiency of man-made object detection on the natural background is reached by anomaly detection in the local structure of images with the use of the Harris response function.