Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

To ensure the power system operates safely and reliably, it is essential to monitor and evaluate the health condition of power equipment on-line or off-line. This study reviews the research status in condition monitoring and diagnosis of power equipment, including transformer, gas insulated switchgear, cable, external insulation, generator, and power capacitor in recent years. Although much progress has been made in technologies of condition monitoring and fault diagnosis such as test accuracy, fast and accurate fault localisation and recognition of fault types, there are still many deficiencies which needs further research work, including the reliability of signal collection from sensors, the accuracy of data treatment and analysis, anti-interference performance of test equipment, appropriate models used for condition evaluation. The prospective of condition monitoring and diagnosis technologies of power equipment are also presented in this study. It is proposed that the application of big data, internet of things and cloud computing should be expected and given special attention in the near future.

Condition monitoring and diagnosis of power equipment: review and prospective

Power transmission line inspection robots: A review, trends and challenges for future research

Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m.

https://www.mdpi.com/2072-4292/9/8/824/pdf

Automatic Power Line Inspection Using UAV Images

When the distance between an obstacle and a power line is less than the discharge distance, a discharge arc can be generated, resulting in the interruption of power supplies. Therefore, regular safety inspections are necessary to ensure the safe operation of power grids. Tall vegetation and buildings are the key factors threatening the safe operation of extra high voltage transmission lines within a power line corridor. Manual or laser intensity direction and ranging (LiDAR) based inspections are time consuming and expensive. To make safety inspections more efficient and flexible, a low-altitude unmanned aerial vehicle (UAV) remote-sensing platform, equipped with an optical digital camera, was used to inspect power line corridors. We propose a semi-patch matching algorithm based on epipolar constraints, using both the correlation coefficient (CC) and the shape of its curve to extract three dimensional (3D) point clouds for a power line corridor. We use a stereo image pair from inter-strip to improve power line measurement accuracy by transforming the power line direction to an approximately perpendicular to epipolar line. The distance between the power lines and the 3D point cloud is taken as a criterion for locating obstacles within the power line corridor automatically. Experimental results show that our proposed method is a reliable, cost effective, and applicable way for practical power line inspection and can locate obstacles within the power line corridor with accuracy better than ±0.5 m.

https://www.mdpi.com/2220-9964/6/1/14/pdf

UAV Low Altitude Photogrammetry for Power Line Inspection

The concept of ecosystem services (ES) supply and demand has attracted increasing attention in science and policy making because it effectively links ecosystem services to human well-being. The imbalance of ES supply and demand in urban areas has become a key issue in regional sustainable development. In this context, we calculated ES supply and demand for Wuhan City, China, using the ES supply and demand ratio (ESDR) and the comprehensive ES supply-demand ratio (CESDR) to express the relationship between ES supply and demand. Ecological zoning was proposed according to the spatial differentiation of the ES supply-demand relationship, and policy recommendations are made. The results show that from the perspective of total ES supply and demand, the water yield supply (SWY), grain yield supply (SGY), and recreation services supply (SRS) are greater than the water yield demand (DWY), grain yield demand (DGY), and recreation services demand (DRS), and that the climate regulation supply (SCR) is less than the climate regulation demand (DCR). From a spatial perspective, there are imbalances and mismatches in ES supply and demand, especially in urban central areas. The values of SWY, SGY, SCS, and SRS per unit area are less than their respective demand values, and the area of mismatch has expanded with the gradual increase of the built-up area. The spatial pattern of ES supply and demand is circular, with the form of "deficit zone-relative equilibrium zone-surplus zone", which corresponds to "urban central area-near suburbs-distant suburbs and rural areas".

https://www.mdpi.com/1660-4601/16/13/2332/pdf

Evaluating Ecosystem Services Supply and Demand Dynamics and Ecological Zoning Management in Wuhan, China.

Feature matching aims to find corresponding points to serve as tie points between images. Robust matching is still a challenging task when input images are characterized by low contrast or contain repetitive patterns, occlusions, or homogeneous textures. In this paper, a novel feature matching algorithm based on graph theory is proposed. This algorithm integrates both geometric and radiometric constraints into an edge-weighted (EW) affinity tensor. Tie points are then obtained by high-order graph matching. Four pairs of poor textural images covering forests, deserts, bare lands, and urban areas are tested. For comparison, three state-of-the-art matching techniques, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), and features from accelerated segment test (FAST), are also used. The experimental results show that the matching recall obtained by SIFT, SURF, and FAST varies from 0 to 35% in different types of poor textures. However, through the integration of both geometry and radiometry and the EW strategy, the recall obtained by the proposed algorithm is better than 50% in all four image pairs. The better matching recall improves the number of correct matches, dispersion, and positional accuracy.

Poor textural image tie point matching via graph theory

When the distance between an obstacle and a power line is less than the discharge distance, a discharge arc can be generated, resulting in interruption of power supplies. Therefore, regular safety inspections are necessary to ensure safe operations of power grids. Tall vegetation and buildings are the key factors threatening the safe operation of extra high voltage transmission lines within a power line corridor. Manual or LiDAR based-inspections are time consuming and expensive. To make safety inspections more efficient and flexible, a low-altitude unmanned aerial vehicle remote-sensing platform equipped with optical digital camera was used to inspect power line corridors. We propose a semi-patch matching algorithm based on epipolar constraints using both correlation coefficient and the shape of its curve to extract three dimensional (3D) point clouds for a power line corridor. Virtual photography was used to transform the power line direction from approximately parallel to the epipolar line to approximately perpendicular to epipolar line to improve power line measurement accuracy. The distance between the power lines and the 3D point cloud is taken as a criterion for locating obstacles within the power line corridor automatically. Experimental results show that our proposed method is a reliable, cost effective and applicable way for practical power line inspection, and can locate obstacles within the power line corridor with measurement accuracies better than ±0.5 m.

https://www.preprints.org/manuscript/201608.0048/v1/download

Shiyu Chen

Papers

Automatic Power Line Inspection Using UAV Images

UAV Low Altitude Photogrammetry for Power Line Inspection

Evaluating Ecosystem Services Supply and Demand Dynamics and Ecological Zoning Management in Wuhan, China.

Poor textural image tie point matching via graph theory

UAV Low Altitude Photogrammetry for Power Line Inspection