The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches

Automating jellyfish species recognition through faster region-based convolution neural networks

In recent years, citizen science campaigns have provided a very good platform for widespread data collection. Within the marine domain, jellyfish are among the most commonly deployed species for citizen reporting purposes. The timely validation of submitted jellyfish reports remains challenging, given the sheer volume of reports being submitted and the relative paucity of trained staff familiar with the taxonomic identification of jellyfish. In this work, hundreds of photos that were submitted to the “Spot the Jellyfish” initiative are used to train a group of region-based, convolution neural networks. The main aim is to develop models that can classify, and distinguish between, the five most commonly recorded species of jellyfish within Maltese waters. In particular, images of the Pelagia noctiluca, Cotylorhiza tuberculata, Carybdea marsupialis, Velella velella and salps were considered. The reliability of the digital architecture is quantified through the precision, recall, f1 score, and κ score metrics. Improvements gained through the applicability of data augmentation and transfer learning techniques, are also discussed. Very promising results, that support upcoming aspirations to embed automated classification methods within online services, including smart phone apps, were obtained. These can reduce, and potentially eliminate, the need for human expert intervention in validating citizen science reports for the five jellyfish species in question, thus providing prompt feedback to the citizen scientist submitting the report.peer-reviewe

Randomize to Generalize: Domain Randomization for Runway FOD Detection

Tiny Object Detection is challenging due to small size, low resolution, occlusion, background clutter, lighting conditions and small object-to-image ratio. Further, object detection methodologies often make underlying assumption that both training and testing data remain congruent. However, this presumption often leads to decline in performance when model is applied to out-of-domain(unseen) data. Techniques like synthetic image generation are employed to improve model performance by leveraging variations in input data. Such an approach typically presumes access to 3D-rendered datasets. In contrast, we propose a novel two-stage methodology Synthetic Randomized Image Augmentation (SRIA), carefully devised to enhance generalization capabilities of models encountering 2D datasets, particularly with lower resolution which is more practical in real-world scenarios. The first stage employs a weakly supervised technique to generate pixel-level segmentation masks. Subsequently, the second stage generates a batch-wise synthesis of artificial images, carefully designed with an array of diverse augmentations. The efficacy of proposed technique is illustrated on challenging foreign object debris (FOD) detection. We compare our results with several SOTA models including CenterNet, SSD, YOLOv3, YOLOv4, YOLOv5, and Outer Vit on a publicly available FOD-A dataset. We also construct an out-of-distribution test set encompassing 800 annotated images featuring a corpus of ten common categories. Notably, by harnessing merely 1.81% of objects from source training data and amalgamating with 29 runway background images, we generate 2227 synthetic images. Subsequent model retraining via transfer learning, utilizing enriched dataset generated by domain randomization, demonstrates significant improvement in detection accuracy. We report that detection accuracy improved from an initial 41% to 92% for OOD test set.Comment: 29 pages, 9 figure

MOBILE WEB APPLICATION PURWOKERTO TRADITIONAL FOOD GAME CLASIFICATION USING MOBILENET V2

Indonesia is a large country where there are thousands of aspects of culture, language and tourism. All of these aspects are an identity for the Indonesian state and each region within it. Culinary is one aspect that is included in the field of tourism. In Indonesia, each region has a special food that is an icon of the area. With so many foods from foreign countries entering Indonesia, this is feared will make the younger generation lose their identity about the regional heritage in special foods. Current technological developments have become excellent in various fields to solve the challenges that exist in the surrounding environment, it does not rule out the possibility that technology can be used to preserve the special foods that exist in each region. Based on the problems outlined above, this research will build a mobile web-based application for the introduction of local specialties through imagery and implement computer vision to mobile devices with CNN MobileNet V2 architecture. In this study a mobile web application was produced that was able to recognize Purwokerto's special foods that could be run well on various devices and operating systems

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop

Automatic Detection and Calculation of Palm Oil Fresh Fruit Bunches using Faster R-CNN

Indonesia is one of the countries with the largest industry of crude palm oil (CPO) in the world. During 2013-2017, the growth of the area of oil palm plantations in Indonesia decreased -0.52%, the decline is expected not to affect the amount of CPO production. One of the things that affect CPO production is the primary raw material availability of palm oil fresh fruit bunches (FFB). Raw material requirements can be predicted by several forecasting methods, but the methods only predict the raw material requirements FFB, not the availability. The development of deep learning eases humans in doing things. Deep learning can be used to calculate FFB automatically using the faster R-CNN algorithm. This study presented a system of automatic detection and calculation of FFB. The evaluation is carried out by comparing 4 network architectures; resnet inception V2, inception V2, resnet 50, and resnet 101. The results of this study indicate success in calculating FFB. The success is indicated by the results of evaluating the four network models with the average F1 scores above 80%

Deep Learning for Recognition of Javanese Batik Patterns

Batik is one of the cultural heritages of a special Indonesian nation. Because of its diversity and uniqueness on October 2, 2009, Batik was first established as Masterpieces of the Oral and Intangible Heritage Humanity by UNESCO. To maintain sustainability, continuous research is needed. Although the topic of research on batik is already common, the introduction of batik patterns still has challenges that need to be resolved. One of the challenges of pattern recognition is in terms of classifying batik motifs. To simplify the work of computers in classifying, in this case the implementation of deep learning is needed by using the convolutional neural network (CNN) method. The convolutional neural network (CNN) method is one of the architectures in deep learning, this method is more effective for classifying images such as batik patterns because the convolutional neural network method has a convolution operation. In this operation the image will be extracted every feature so that it can produce patterns that can facilitate classification. In the process of training the convolutional neural network method requires heavy computation and not a short amount of time, therefore the use of GPU performance is needed to speed up the training time. The experimental process begins by compiling five classes of data sets of batik images, the class consisting of batik parang rusak, batik kawung, batik nitik, batik ceplok, and batik lereng with a total of 750 batik images as data sets. The data set was then trained using the Python programming language and GPU CUDA. The test results using cross-validation can achieve an accuracy of 90.14%. So that the results of the above tests can be concluded that deep learning using the CNN method can be used to classify batik patterns well

Noncontact sensing systems and autonomous decision-making for early-age concrete

Early-age cracking and spalling in concrete pavements reduces slab capacity, joint load transfer, ride quality, and its long-term performance. These premature distresses lead to increased maintenance costs for sealing, patching, and grinding. Proper timing of sawcutting and curing are two construction activities that can minimize early-age distress development. In order to better time sawcutting and curing activities, an improved method to spatially monitor the setting time of concrete is required. Likewise, rapid evaluation of the joint quality after sawing is also necessary to provide feedback to adjust the timing. While previous methods for sawcutting and curing are experiential and subjective, this research aims to develop contactless sensing and computer vision techniques to significantly improve the timing of certain early-age concrete construction activity decisions through quantitative indicators. A non-contact, ultrasonic testing system (UTS) to monitor concrete set time has been developed by monitoring the evolution of leaky Rayleigh (LR-wave) wave signals over time and space (surface of the concrete). The non-contact UTS integrates a 50 kHz non-contact ultrasonic transmitter and an array of five microelectromechanical systems (MEMS) sensors as non-contact receivers. The UTS technique was first implemented in the laboratory at incident angles of 12^° for mortar mixtures in order to determine the final setting times. The UTS technique was also applied at different incident angles (12^° to 60^° ) on a mortar mixture to evaluate its influence of the angle on the UTS measurement. The final setting times for mortars were consistent with the ASTM C403 penetration resistance standard when an incident angle of 12^° was used. Additionally, this UTS was successfully field validated on three concrete pavement test sections in Illinois that had different casting times during the day. Final setting times in the field greatly varied (287 to 210 minutes) given the higher ambient temperatures and surrounding concrete mass. In order to improve decision-making on sawcut timing, the final set times measured by the UTS were linked with the earliest time to initiate sawcutting within an acceptable level of raveling. A computer vision-based (CV) process was developed that employed multiple joint images, 2D segmentation for joint raveling/spalling extraction, 3D point cloud reconstruction and meshing of the joint damage, and a 3D damage quantification analysis for assessing the joint damage. The proposed CV-based joint damage analysis quantified joint damage through two newly defined indices: (i) raveling damage index (RDI) for raveling and (ii) joint damage index (JDI) for spalling. The proposed CV-based method had an accuracy of 76% with an error of 10%. With this CV-based process, it was determined that RDI of 3% or less is an acceptable quality level for contraction joints in the field. A one-sided multi-sensor ultrasonic array device with a support vector machine algorithm was developed that detects the existence of a concealed, vertical crack beneath a notched contraction joint. This algorithm supports the field assessment of the effectiveness of sawcut timing, sawcut depth, and whether premature slab cracking was related to poor sawing procedures. The multi-sensor ultrasonic array device generated and received ultrasonic shear waves (S-wave) across the inspected joint. The acquired time domain signals were used to calculate normalized transmission energy (NTE) across the joint. The NTE algorithm defined the ratio of the energy of diffracted and reflected S-waves received behind the joint with respect to the energy of direct, diffracted, and reflected S-waves received in front of the joint. Laboratory results demonstrated that the NTE technique could successfully identify the existence or non-existence of a crack beneath the sawcut. Finally, the NTE technique coupled with a 2D decision boundary equation was field validated on 152 concrete pavement contraction joints from multiple projects with similar slab thicknesses and sawcut notch depths in Illinois and Iowa. Finally, the non-contact UTS was coupled with a 2D wavefield analysis to rapidly evaluate the effectiveness, spatially and with time, of curing methods through monitoring of the near-surface damage in hydrating paste at early-ages. The new technique monitored the energy of the LR-waves signal over time with the non-contact UTS and then, analyzed the frequency-wave number (f-k) domain to characterize the quantity of near-surface damage in the cement paste specimens. An ultrasonic surface damage index (USDI) was defined from the f-k wavefield domain based on the ratio of the non-propagating and forwarding LR-wave energy. The non-contact sensing and 2D wavefield analysis easily distinguished the differences in surface damage between the different curing methods (no curing surface, the plastic sheet cover cure, and the wax-based curing). Surfaces with low surface damage had negligible non-propagating wave energy, which was seen in the wax-based curing specimens and the unexposed bottom surfaces of all cast specimens

A Systematic Review on Object Localisation Methods in Images

[EN] Currently, many applications require a precise localization of the objects that appear in an image, to later process them. This is the case of visual inspection in the industry, computer-aided clinical diagnostic systems, the obstacle detection in vehicles or in robots, among others. However, several factors such as the quality of the image and the appearance of the objects to be detected make this automatic location difficult. In this article, we carry out a systematic revision of the main methods used to locate objects by considering since the methods based on sliding windows, as the detector proposed by Viola and Jones, until the current methods that use deep learning networks, such as Faster-RCNN or Mask-RCNN. For each proposal, we describe the relevant details, considering their advantages and disadvantages, as well as the main applications of these methods in various areas. This paper aims to provide a clean and condensed review of the state of the art of these techniques, their usefulness and their implementations in order to facilitate their knowledge and use by any researcher that requires locating objects in digital images. We conclude this work by summarizing the main ideas presented and discussing the future trends of these methods.[ES] Actualmente, muchas aplicaciones requieren localizar de forma precisa los objetos que aparecen en una imagen, para su posterior procesamiento. Este es el caso de la inspección visual en la industria, los sistemas de diagnóstico clínico asistido por computador, la detección de obstáculos en vehículos o en robots, entre otros. Sin embargo, diversos factores como la calidad de la imagen y la apariencia de los objetos a detectar, dificultan la localización automática. En este artículo realizamos una revisión sistemática de los principales métodos utilizados para localizar objetos, considerando desde los métodos basados en ventanas deslizantes, como el detector propuesto por Viola y Jones, hasta los métodos actuales que usan redes de aprendizaje profundo, tales como Faster-RCNNo Mask-RCNN. Para cada propuesta, describimos los detalles relevantes, considerando sus ventajas y desventajas, así como sus aplicaciones en diversas áreas. El artículo pretende proporcionar una revisión ordenada y condensada del estado del arte de estas técnicas, su utilidad y sus implementaciones a fin de facilitar su conocimiento y uso por cualquier investigador que requiera localizar objetos en imágenes digitales. Concluimos este trabajo resumiendo las ideas presentadas y discutiendo líneas de trabajo futuro.Este trabajo ha sido financiado parcialmente por diferentes instituciones. Deisy Chaves cuenta con una beca “Estudios de Doctorado en Colombia 2013” de COLCIENCIAS. 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Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically