Comparison of deep learning and analytic image processing methods for autonomous inspection of railway bolts and clips

In this work, different methods are proposed and compared for autonomous inspection of railway bolts and clips. A prototype of an autonomous data acquisition system was developed to automatically obtain information of the state of the railway track using LiDAR and camera sensors. This system was employed in a testing railway track installed in the facilities of the University of Vigo to obtain the images used in this work. Then, the images were further processed using analytic image segmentation algorithms as well as a neural network to detect the bolts and clips. Once these elements are detected, their relative position is computed to evaluate if there is any missing component. Finally, the orientation of the clips is computed to ensure that all the bolts are correctly placed. Four different methods were implemented, and their performance was evaluated using the segmentations provided by the analytical methods and the neural network.Ministerio de Universidades | Ref. FPU21/01176Ministerio de Ciencia e Innovación | Ref. PLEC2021-007940Recovery, Transformation and Resilience Plan of the European Union – NextGenerationEU (University of Vigo) | Ref. 58550

Enhancing UAV Classification with Synthetic Data: GMM LiDAR Simulator for Aerial Surveillance Applications

The proliferation of Unmanned Aerial Vehicles (UAVs) has raised safety concerns due to the potential threats resulting from their misuse or malicious intent. Due to their compact size, high-resolution surveillance systems such as LiDAR sensors are necessary to exert effective control over the airspace. Given the large volume of data that these technologies generate, efficient Deep Learning (DL) algorithms are needed to make their real-time implementation feasible. However, the training of DL models requires extense and diverse datasets, which in certain scenarios may not be available. Therefore, this work introduces a novel method based on Gaussian Mixture Models (GMMs) for simulating realistic synthetic point clouds of UAVs. This simulator is calibrated using experimental data and allows to probabilistically replicate the intricacies of sensor ray propagation, thereby addressing the limitations of current Ray Tracing (RT) simulators such as Gazebo or CARLA. In this study, we perform a quantitative analysis of the point cloud quality of the GMM simulator, comparing it with the results obtained using a RT approach. Additionally, we evaluate the effectiveness of both methods in training object classifiers. Results demonstrate the GMM simulator’s potential for creating realistic synthetic databasesAgencia Estatal de Investigación | Ref. PID2021-125060OB-100Agencia Estatal de Investigación | Ref. TED2021-129757B-C3Ministerio de Universidades | Ref.FPU21/0117

UAV and satellite imagery applied to alien species mapping in NW Spain

Image classification stands as an essential tool for automated mapping, that is demanded by agencies and stakeholders dealing with geospatial information. Decreasing costs or UAV-based surveying and open access to high resolution satellite images such as that provided by European Union’s Copernicus programme are the basis for multi-temporal landscape analysis and monitoring. Besides that, invasive alien species are considered a risk for biodiversity and their inventory is needed for further control and eradication. In this work, a methodology for semi-automatic detection of invasive alien species through UAV surveying and Sentinel 2 satellite monitoring is presented and particularized for Acacia dealbata Link species in the province of Pontevedra, in NW Spain. We selected a scenario with notable invasion of Acaciae and performed a UAS surveying to outline feasible training areas. Such areas were used as bounds for obtaining a spectral response of the cover from Sentinel 2 images with a level of processing 2A, that was used for invasive area detection. Sparse detected areas were treated as a seed for a region growing step to obtain the final map of alien species.Deputación de Pontevedra | Ref. 17/410.1720.789.0

Point cloud simulator for space in-orbit close range autonomous operations

In recent years, many different in-orbit close-range autonomous operations have been developed for multiple purposes, such as rendezvous and docking operations or ADR operations. In both cases, the systems have to calculate the relative position between the spacecraft and the target in order to control the orbital manoeuvres and the physic interaction between both systems. One of the sensors used for the pose calculation for these operations are LiDAR sensors, developing pose calculation algorithms that process the point cloud acquired by these sensors. One of the main problems for the development and testing of these algorithms is the lack of real data acquired in orbit and the difficulty of acquiring this data. This makes it fundamental to develop a simulator to generate realistic point clouds that can be used to develop and test pose calculation algorithms. This work presents a simulator developed for this purpose, that is the generation of realistic point clouds for algorithm development for pose calculation using LiDAR sensors for space in-orbit close range autonomous operations. The simulator uses the LiDAR sensor specifications, in order to introduces measurement errors and the scanning pattern, and 3D model of the satellite or object that is scanned.Universidade de Vigo | Ref. 58550

Operational study of drone spraying application of phytosanitary products in vineyards

The use of drones in topics related to precision agriculture to improve the efficiency in the application of phytosanitary products to vineyards increases every day. Drones are especially productive in difficult orographic terrains, where other mechanical systems such as tractors cannot be used. This study shows the development and implementation of a methodology to determine key parameters to decide the suitability of a drone to a spraying task (i.e. spraying time for a certain parcel, number or tank refills required), taking into account the technical specifications of a certain commercial model. For the validation, the data of a vineyard belonging to the Rías Baixas appellation of origin (NW Spain) and the technical specifications of drones from three different manufacturers (i.e. DJI, Hylio and Yamaha) are used. Results show that the Hylio AD122 with a phytosanitary tank of 22 L provides the best performance, with a productivity around 6 minutes per hectare.La utilización de drones en tareas relacionadas con la agricultura de precisión para mejorar la eficiencia en la aplicación de productos fitosanitarios en viñedos es cada vez mayor. Los drones son especialmente eficientes en terrenos con orografía difícil, donde no se pueden emplear otros sistemas mecánicos como tractores. Este estudio muestra el desarrollo e implementación de una metodología para determinar parámetros clave que decidan la adecuación de un drone determinado a una tarea de fumigación (por ejemplo, el tiempo de fumigación para una cierta parcela o el número de tanques requeridos para dicha fumigación), teniendo en cuenta las especificaciones técnicas de un determinado modelo comercial. Para la validación de la metodología, se han utilizado los datos de un viñedo que pertenece a la denominación de origen Rías Baixas (Noroeste de Espala) y las características técnicas de tres fabricantes diferentes de drones (DJI, Hylio y Yamaha). Los resultados obtenidos muestran como el Hylio AD122 con un tanque de fitosanitario de 22 L provee el mejor rendimiento, con una productividad de aproximadamente 6 minutos por hectáre

Camera sensor arrangement for crop/weed detection accuracy in agronomic images

In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects. © 2013 by the authors; licensee MDPI, Basel, Switzerland.The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement NO.245986. This paper has been extended from a previous paper published in [20]. The authors wish also to acknowledge to the project AGL2011-30442-C02-02, supported by the Ministerio de Economía y Competitividad of Spain within the Plan Nacional de I+D+i.Peer Reviewe

Slackline Training in Children with Spastic Cerebral Palsy: A Randomized Clinical Trial

[EN] Objective: To assess whether a slackline intervention program improves postural control in children/adolescents with spastic cerebral palsy (CP). Design: Randomized controlled trial. Setting: Patients’ association. Participants: Twenty-seven children/adolescents with spastic CP (9–16 years) were randomly assigned to a slackline intervention (n = 14, 13 ± 3 years) or control group (n = 13, 12 ± 2 years ). Intervention: Three slackline sessions per week (30 min/session) for 6 weeks. Main outcome measures: The primary outcome was static posturography (center of pressure—CoP—parameters). The secondary outcomes were surface myoelectrical activity of the lower-limb muscles during the posturography test and jump performance (countermovement jump test and Abalakov test). Overall (RPE, >6–20 scale) rating of perceived exertion was recorded at the end of each intervention session. Results: The intervention was perceived as “very light” (RPE = 7.6 ± 0.6). The intervention yielded significant benefits on static posturography (a significant group by time interaction on Xspeed, p = 0.006) and jump performance (a significant group by time interaction on Abalakov test, p = 0.015). Conclusions: Slackline training improved static postural control and motor skills and was perceived as non-fatiguing in children/adolescents with spastic CP.S

Laser Shock Processing: An Emerging Technique for the Enhancement of Surface Properties and Fatigue Life of High Strength Metal Alloys

Profiting by the increasing availability of laser sources delivering intensities above 10 9 W/cm 2 with pulse energies in the range of several Joules and pulse widths in the range of nanoseconds, laser shock processing (LSP) is being consolidating as an effective technology for the improvement of surface mechanical and corrosion resistance properties of metals and is being developed as a practical process amenable to production engineering. The main acknowledged advantage of the laser shock processing technique consists on its capability of inducing a relatively deep compression residual stresses field into metallic alloy pieces allowing an improved mechanical behaviour, explicitly, the life improvement of the treated specimens against wear, crack growth and stress corrosion cracking. Following a short description of the theoretical/computational and experimental methods developed by the authors for the predictive assessment and experimental implementation of LSP treatments, experimental results on the residual stress profiles and associated surface properties modification successfully reached in typical materials (specifically steels and Al and Ti alloys) under different LSP irradiation conditions are presente

Psychological distress and resilience of mothers and fathers with respect to the neurobehavioral performance of small-forgestational- age newborns

The existence of psychological distress (PD) during pregnancy is well established. Nevertheless, few studies have analyzed the PD and resilience of mothers and fathers during high-risk pregnancy. This study analyzes the differences between parents’ PD and resilience and the relation between them and the neurobehavioral performance of their SGA newborns. Multivariate analysis of variance showed, in gender comparisons, that mothers obtained higher scores than fathers for psychological distress but lower ones for resilience. Similar differences were obtained in the comparison of parents’ distress to intrauterine growth by SGA vs. AGA newborns. Mothers of SGA newborns were more distressed than the other groups. However, there were no differences between the fathers of SGA vs. AGA newborns. Regarding neurobehavioral performance, the profiles of SGA newborns reflected a lower degree of maturity than those of AGA newborns. Hierarchical regression analyses showed that high stress and low resilience among mothers partially predict low neurobehavioral performance in SGA newborns. These findings indicate that mothers of SGA newborns may need psychological support to relieve stress and improve their resilience. Furthermore, attention should be paid to the neurobehavioral performance of their babies in case early attention is neededThis study was supported by University of Granada (Spain), Andalusian Public Foundation for Biosanitary Research Eastern Andalusia (Spain), and Ministry of Health, Junta de Andalucía (Spain) Award Number: PC-0526-2016-0526