Explore the vast range of titles available.

Photographs have been doctored since photography was invented. Dictators have erased people from photographs and from history. Politicians have manipulated photos for short-term political gain. Altering photographs in the predigital era required time-consuming darkroom work. Today, powerful and low-cost digital technology makes it relatively easy to alter digital images, and the resulting fakes are difficult to detect. The field of photo forensics - pioneered in Hany Farid's lab at Dartmouth College - restores some trust to photography. In this book, Farid describes techniques that can be used to authenticate photos.

The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but time-consuming model, the UAV-LiDAR resulting point cloud’s georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5–10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented.

Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in real applications. In this paper, we propose a novel efficient cross-modality insulator augmentation algorithm for multi-domain insulator defect detection to mimic real complex scenarios. It also alleviates the overfitting problem without adding the inference resources. The high-resolution insulator cross-modality translation (HICT) module is designed to generate multi-modality insulator images with rich texture information to eliminate the adverse effects of existing modality discrepancy. We propose the multi-domain insulator multi-scale spatial augmentation (MMA) module to simultaneously augment multi-domain insulator images with different spatial scales and leverage these fused images and location information to help the target model locate defects with various scales more accurately. Experimental results prove that the proposed cross-modality insulator augmentation algorithm can achieve superior performance in public UPID and SFID insulator defect datasets. Moreover, the proposed algorithm also gives a new perspective for improving insulator defect detection precision without adding inference resources, which is of great significance for advancing the detection of transmission lines.

Purpose The purpose of this paper is to report on the results of an exploratory study on the use of unmanned aerial vehicles (UAV) as a visual data collection tool in the architecture, engineering, construction and facility management industry for the inspection of pathological manifestations in building facades. Design/methodology/approach The methodology used a field research experimental approach, where three case studies were carried out involving buildings of medium and high elevation. The protocol of activities included image collection and processing stages, as well as detailed analysis of the collected visual data for the identification of pathological manifestations in building facades. Findings The findings emphasize the technical feasibility and efficacy of inspections with UAV, showing that among the visual assets produced, digital photographs collected with the aircraft were more effective for the detection of pathologies when compared to the three-dimensional models and orthomosaics generated by digital photogrammetry software. Originality/value The research has formulated the protocol for the inspection of facades using UAV and the comparative analysis of visual assets that can be generated for inspection purposes.

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically.

Background Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated chicory ( Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts. Results Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an r 2 of 0.9217. We also achieve an F 1 of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image. Conclusion We have demonstrated the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method. The success of our approach is also a demonstration of the feasibility of deep learning in practice for small research groups needing to create their own custom labelled dataset from scratch.

The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been increasingly used for safety surveillance in the construction industry and civil engineering fields. This paper presents an aerial image-based approach using UAVs to inspect cracks and deformations in buildings. A state-of-the-art safety evaluation method termed SMART SKY EYE (Smart building safety assessment system using UAV) is introduced; this system utilizes an unmanned airplane equipped with a thermal camera and programmed with various surveying efficiency improvement methods, such as thermography, machine-learning algorithms, and 3D point cloud modeling. Using this method, crack maps, crack depths, and the deformations of structures can be obtained. Error rates are compared between the proposed and conventional methods.

Presents recent significant and rapid development in the field of 2D and 3D image analysis 2D and 3D Image Analysis by Moments, is a unique compendium of moment-based image analysis which includes traditional methods and also reflects the latest development of the field.

A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

Two major earthquakes struck Pazarcık and Elbistan, towns in Kahramanmaraş, Türkiye, on 6 February 2023, approximately 9 h apart. The first earthquake, recorded at 04:17 local time, had a Mw = 7.7, with a focal depth of 8.6 km. At 13:24 local time, a second earthquake occurred with Mw = 7.6 at a focal depth of 7 km, approximately 90 km north of the first one. A total of 11 provinces were severely affected by these earthquakes. As of 15 April 2023, they caused close to 51,000 deaths and almost 215,000 completely destroyed/severely damaged buildings. At some locations, the largest horizontal peak ground acceleration (PGA) values of the first and second earthquakes exceeded the code-generated PGAs by almost 3 and 1.75 times, respectively. A technical team visited these areas within 15 h of the first earthquake. The purpose of this article is to present their observations, findings, and the characteristics of the two earthquakes, with comprehensive site survey results supported by photographs. This study concludes that most of the collapsed and severely/moderately damaged buildings in the region were built between 1975 and 2000, when site inspections were rare or non-existent. In addition to the high PGAs recorded in these earthquakes, it was verified that the design and construction of these buildings did not fully comply with the earthquake codes valid at the time. The collapsed buildings and their damage patterns confirm inadequate development length, violation of bending stirrup ends at 135°, deficiencies in construction materials and reinforcement configuration, noncompliance with confinement zones, violation of the strong beam-stronger column analogy, and issues related to building inspection. Based on the extent of the damage, it is strongly recommended that the structural performance inspection of all other buildings located near major fault lines, specifically those constructed between 1975 and 2000, should be completed. Since these earthquakes generated much higher PGAs, which is believed to be one of the main reasons for the extensive damage, a re-evaluation of all other PGAs along major fault lines is also recommended.