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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.

Stacked cage is the main breeding method of the large-scale farm in China. In broiler farms, dead broiler inspection is a routine task in the breeding process. It refers to the manual inspection of all cages and removal of dead broilers in the broiler house by the breeders every day. However, as the total amount of broilers is huge, the inspection work is not only time-consuming but also laborious. Therefore, a dead broiler inspection system is constructed in this study to replace the manual inspection work. It mainly consists of an autonomous inspection platform and a dead broiler detection model. The automatic inspection platform performs inspections at the speed of 0.2 m/s in the broiler house aisle, and simultaneously collects images of the four-layer broilers. The images are sent to a server and processed by a dead broiler detection model, which was developed based on the YOLOv3 network. A mosaic augment, the Swish function, an spatial pyramid pooling (SPP) module, and complete intersection over union (CIoU) loss are used to improve the YOLOv3 performance. It achieves a 98.6% mean average precision (intersection of union (IoU) = 0.5) and can process images at 0.007 s per frame. The dead broiler detection model is robust to broilers of different ages and can adapt to different lighting conditions. It is deployed on the server with a human–machine interface. By observing the processing results using the human–machine interface, the breeders could directly find the cage position of dead broilers and remove them, which could reduce the workload of breeders and promote the intelligent development of poultry breeding.

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, “on ground crop inspection” potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. “On ground monitoring” is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows.

Purpose Mooring chains used to stabilise offshore floating platforms are often subjected to harsh environmental conditions on a daily basis, i.e. high tidal waves, storms, etc. Therefore, the integrity assessment of chain links is vital, and regular inspection is mandatory for offshore structures. The development of chain climbing robots is still in its infancy due to the complicated climbing structure presented by mooring chains. The purpose of this paper is to establish an automated climbing technique for mooring chain inspection. Design/methodology/approach This paper presents a Cartesian legged tracked-wheel crawler robot developed for mooring chain inspection. The proposed robot addresses the misalignment condition of the mooring chains which is commonly evident in in situ conditions. Findings The mooring chain link misalignment is investigated mathematically and used as a design parameter for the proposed robot. The robot is validated with laboratory-based climbing experiments. Practical implications Chain breaking can lead to vessel drift and serious damage such as riser rupture, production shutdown and hydrocarbon release. Currently, structural health monitoring of chain links is conducted using either remotely operated vehicles which come at a high cost or by manual means which increase the danger to human operators. The robot can be used as a platform to convey equipment, i.e. tools for non-destructive testing/evaluation applications. Originality/value This study has upgraded a previously designed magnetic adhesion tracked-wheel mooring chain climbing robot to address the misalignment issues of operational mooring chains. As a result of this study, the idea of an orthogonally placed Cartesian legged-magnetic adhesion tracked wheel robotic platform which can eliminate concerns related to the misaligned mooring chain climbing has been established.

Huanglongbing (HLB) was first identified near Araraquara in the central region of São Paulo State (SPS), Brazil, in March 2004. As of November 2009, HLB was present in 242 of the 425 citrus-growing municipalities of SPS. In April 2009, the current total number of symptomatic trees was estimated to be ca. 2.0 million (ca. 0.87%) and 4 million trees had already been removed. The recommended measures for HLB management are based on two phytopathologically sound principles: (i) inoculum reduction by frequent removal of HLB-affected trees and (ii) control of psyllid vector populations by insecticide treatments. The goal of this management strategy is to prevent as many trees as possible from becoming infected with the HLB pathogen. After five or six years of HLB management, several SPS citrus farms have shared their results on HLB control. Here we present data from eighteen farms where the recommended measures have been applied since 2004 or 2005 in SPS, showing that HLB can be controlled. SPS is one of the first regions in the world where preventive control against Asian HLB has been carried out on a large scale under various conditions and found to be successful when the recommended measures were applied rigorously. However, only one-third of SPS citrus trees are under effective HLB-management and they are located on large farms, where HLB management is easier than on small farms. The majority of trees in small and medium-sized groves commonly do not benefit from HLB management. This is the reason that HLB incidence in SPS has increased in spite of HLB management. Many groves in which no HLB management is carried out have high proportions of HLB-affected trees and large populations of HLB-positive psyllid vectors. These groves are the major obstacle to HLB management in SPS. These severely affected groves endanger the very existence of the Brazilian citrus industry. When such groves are within a ca. 4km range from well-man-aged farms, their psyllids invariably invade and contaminate the latter farms. SPS has legal tools, which make possible the removal of contaminating groves, but the laws are not strictly enforced. Costs of HLB management vary considerably, but inspections range from $4 to 17 $US each per ha, and insecticide treatments from about $US 240 to > $1,000 per ha annually, depending on the products used, the means and frequency of application. HLB management as described here is only a short-term solution to keep the citrus industry alive and to buy time for long-term solutions, probably based on engineered citrus genotypes, to become available, hopefully, in five to ten years.

With the continuous improvement of modern surveying and mapping technology and with the plentiful of achievements, traditional quality inspection software for single machine, single task and single data type, difficult to massive multi-source isomerization achievements, difficult to meet the requirement of rapid, accurate and efficient quality inspection. With the development of IT technology such as cloud computing, big data and artificial intelligence, the quality inspection software needs to combine cloud computing technology with quality inspection business, refactoring software framework. Facing to the storage and spatial query requirement of inspection for surveying and mapping products, the paper researches and designs the spatial data distributed storage and the spatial data distributed index in cloud platform. The Management of inspection rule is the core in cloud platform. Inspection rule is the minimum operating independent unit, which becomes inspection item by parameterization, the paper builds full run-time operating mechanism in cloud platform for inspection rule. Finally, Combining the inspection requirement for surveying and mapping products and business, the paper researches and design the cloud framework for surveying and mapping products.

Current railway tunnel inspections rely on expert operators performing a visual examination of the entire infrastructure and manually annotating encountered defects. Automatizing the inspection and maintenance task of such critical and aging infrastructures has the potential to decrease the associated costs and risks. Contributing to this aim, the present work describes an aerial robotic solution designed to perform autonomous inspections of tunnel-like infrastructures. The proposed robotic system is equipped with visual and thermal sensors and uses an inspection-driven path planning algorithm to generate a path that maximizes the quality of the gathered data in terms of photogrammetry goals while optimizing the surface coverage and the total trajectory length. The performance of the planning algorithm is demonstrated in simulation against state-of-the-art methods and a wall-following inspection trajectory. Results of a real inspection test conducted in a railway tunnel are also presented, validating the whole system operation.

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.

The platform screen doors of rail transit are the connecting parts between trains and platforms. During operation, the manual push type crowd squeezing pressure loading inspection equipment has problems of low accuracy and low manual control efficiency. In this research, the technology, the automatic control of airbag inflatable loading and inspection technology, was carried out. It uses an air compressor as the air source, and through a pressure monitoring and control module, outputs signals to control airbag pressurization and exhaust pressure relief. The pressurization speed is adjustable, and deformation data of platform door components is detected at the same time. The experimental and numerical results indicate that the loading width of the detection equipment covers all platform door specifications, and the loading is uniform, and meets the linear load requirements for simulating crowd squeezing in the standard. In addition, the research result could effectively improve the authenticity and detection efficiency of platform door simulation detection, and reduce the investment of manpower and material resources, which is helpful for laboratory and on-site testing of platform doors.

On September 5, 2022, an Ms6.8 earthquake struck Luding County, Sichuan Province, China. Through creating a coseismic landslide prediction model, we obtained the spatial distribution of the triggered geological hazards immediately after the earthquake. Through collecting all available multi-source optical remote sensing images of the earthquake-affected area via UAV and satellite platforms, the exact information of coseismic landslide was achieved by pattern recognition and visual inspection. According to the current results, the Luding earthquake triggered 5336 landslides with a total area of 28.53km2. The spatial distribution of the coseismic landslides is correlated statistically to various seismic, terrain, and geological factors, to evaluate their susceptibility at regional scale and to identify the most typical characteristics of these failures. The results reveal that the coseismic landslides mainly occurred on the sides of the Xianshuihe fault (within 1.2 km) and Dadu River (within 0.5 km) in striped patterns. They are concentrated in the regions with an elevation range of 1000–1800 m, a slope range of 25–55°, and lithologies of acid plutonic rocks, mixed sedimentary rocks, and siliciclastic sedimentary rocks. Besides, the coseismic landslides of the Luding earthquake are smaller in size and shallower than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake. Rapidly achieving the spatial locations and distribution patterns of the coseismic landslides enables to provide effective support and guidance to emergency rescue, risk mitigation, and reconstruction planning.