Unmanned Aerial Vehicle Inspection Research Articles

Distributed decision making for unmanned aerial vehicle inspection with limited energy constraint

The unsatisfactory energy density of the state-of-art batteries imposes constraints on the practical application of unmanned aerial vehicles (UAVs). Establishing a UAV airport network that integrates energy supply and information exchange functionalities represents an ideal solution for enabling synergistic UAV operations. However, devising efficient distribution protocols for these airports remains a challenge. By leveraging modeling and analysis of the energy density of existing UAV batteries, we can forecast the flight range and distances achievable by UAVs. Here, we propose a distribution protocol for UAV airport platforms aimed at enhancing distribution accuracy by the use of AI principles. Furthermore, considering the possibility of emergency UAV stop, we introduce an emergency stop system in conjunction with standard stopping procedures to optimize distribution efficiency and enhance UAV inspection safety. Moreover, existing UAV airports usually provide energy to UAVs without harnessing UAVs to facilitate interconnection and interoperability among different airports. This inefficiency leads to significant resource wastage in energy distribution. To address this, we introduce a shared energy network that allows different companies to operate according to energy distribution needs. This network not only supplies energy to UAVs but also employs UAVs for energy collection and transportation, facilitating energy trading, business collaboration, and data transmission among diverse organizations. By enabling ubiquitous energy trading, this study provides us an ideal strategy for the future construction of energy network with interconnection and interoperability, which can be extended to other applications calling for energy distribution.

Optimization for total energy consumption of drone inspection based on distance-constrained capacitated vehicle routing problem: A study in wind farm

Facing large-scale, intensive offshore wind farms in wide and deep sea areas, total inspection energy consumption (TIEC) optimization is a key issue to be addressed in unmanned aerial vehicle (UAV) inspection. This paper focuses on the TIEC optimization of UAV automatic inspection of offshore wind farms based on the assistance of mobile edge computing (MEC). To tackle this issue, a system model using a UAV to inspect offshore wind farms automatically and an energy consumption model of UAV assisted by MEC servers and near-earth orbiting satellites for inspecting wind turbines are proposed. Based on this, a combinatorial optimization problem based on the distance-constrained capacitated vehicle routing problem (DCVRP) for minimizing the TIEC of UAV inspection was constructed. To solve the optimization problem, an improved hybrid heuristic algorithm is proposed based on the K-means clustering, smallest enclosing circle (SEC), and Lin-Kernighan-Helsgaun (LKH-3) algorithms. The algorithm solves the optimum location and number of automated UAV airports and minimizes the TIEC for UAV inspection. The simulation experimental results show that in an offshore wind farm with 86 wind turbines, compared with other existing schemes, this scheme requires only four automated UAV airfields and can save at most 51.8% of the TIEC.

Autonomous Crack Detection for Mountainous Roads Using UAV Inspection System.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×106) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

UAV‐aided distribution line inspection using double‐layer offloading mechanism

AbstractWith the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.

Real-Time Object Detection from UAV Inspection Videos by Combining YOLOv5s and DeepStream.

The high-altitude real-time inspection of unmanned aerial vehicles (UAVs) has always been a very challenging task. Because high-altitude inspections are susceptible to interference from different weather conditions, interference from communication signals and a larger field of view result in a smaller object area to be identified. We adopted a method that combines a UAV system scheduling platform with artificial intelligence object detection to implement the UAV automatic inspection technology. We trained the YOLOv5s model on five different categories of vehicle data sets, in which mAP50 and mAP50-95 reached 93.2% and 71.7%, respectively. The YOLOv5s model size is only 13.76 MB, and the detection speed of a single inspection photo reaches 11.26 ms. It is a relatively lightweight model and is suitable for deployment on edge devices for real-time detection. In the original DeepStream framework, we set up the http communication protocol to start quickly to enable different users to call and use it at the same time. In addition, asynchronous sending of alarm frame interception function was added and the auxiliary services were set up to quickly resume video streaming after interruption. We deployed the trained YOLOv5s model on the improved DeepStream framework to implement automatic UAV inspection.

Research on collaborative inspection key technologies for multi-UAVs and multi-nests

The single individual capability and insufficient endurance of unmanned aerial vehicles (UAVs) have always been problems in power inspection operations. This article is based on the new development needs of UAV inspection and proposes a new model of multi-UAVs collaborative inspection. On this basis, this article studies the decoupling and intelligent matching technology between airports and UAVs, task merging and splitting technology in complex multi-task situations, and proposes a safety inspection strategy for multi-UAVs collaborative operations to ensure the safety of operations in the multi-UAVs multi-nests collaborative inspection mode. Finally, a complete multi-UAV, multi-nests collaborative inspection strategy is constructed.

UAV vision-based crack quantification and visualization of bridges: system design and engineering application

Accurately measuring visible cracks in bridges is crucial for their structural health diagnosis, damage detection, performance evaluation, and maintenance planning. The primary means of visual crack detection still relies heavily on manual visual inspection, an inefficient process that can pose significant safety risks. This article develops a unmanned aerial vehicle (UAV) vision-based surface crack measurement methodology and visualization scheme for the bridges that can detect and measure cracks automatically with improved efficiency. The surface crack measurement methodology is achieved by designing a three-stage crack sensing system including the You Only Look Once-based crack recognition, U-shaped network-based crack segmentation, and deep-vision-based crack width calculation. This workflow is integrated into a comprehensive UAV inspection system, which is intended for operation at the field. The surface crack visualization scheme is accomplished by taking advantage of time-series image fusion, GPS information migration, and three-dimensional (3D) point cloud technique to reconstruct the 3D geometrical model of the tested bridge, which is convenient for unveiling the crack information in the bridge. The proposed methodology was successfully validated by a case study on an arch bridge. The achievement of this article promotes the UAV vision-based bridge’s surface crack inspection technology to a new status that no preparation for pasting calibration marker is needed, and crack identification, segmentation, and width calculation are realized promptly during the UAV flying on-site, as well as damage evaluation for bridges is visually fulfilled based on the reconstructed digital-graphical 3D model. The working environments and influencing factors to the developed system are sufficiently discussed. Certain limitations in the current application are pointed out for future improvements.

Development and validation of graphics‐based digital twin framework for UAV‐aided post‐earthquake inspection of high‐rise buildings

SummaryTraditional post‐earthquake inspection of civil infrastructure is conducted manually, taking a considerable amount of time and often putting inspectors in harm's way. This problem is exacerbated in modern cities, where millions of people can be left homeless until their residences are deemed safe to reinhabit. Image collection enabled by commercial unmanned aerial vehicles (UAVs) combined with computer vision‐based techniques has provided an alternative with high potential for rapid post‐earthquake inspection. However, the extracted images of the damage alone are inadequate to evaluate the system‐level safety condition of a structure. The quality of the visual information also heavily relies on the effectiveness of the UAV inspection scheme which is susceptible to environmental uncertainties. To this end, a graphics‐based digital twin (GBDT) framework is developed for UAV‐aided post‐earthquake inspection of high‐rise buildings and validated using a high‐rise building in Guangzhou, China. The GBDT is comprised of a finite element (FE) model and a photorealistic computer graphics (CG) model, with the latter being informed by the former, jointly providing as a comprehensive virtual representation of the structure so that every step of the post‐earthquake inspection procedure can be planned and evaluated virtually. First, to avoid the cumbersome nature of constructing the graphical representation of the numerous components in high‐rise buildings, the CG model in the GBDT is created by automatically importing structural components from the FE model and adding nonstructural components according to the dimensions of the as‐built structure. This fast modeling process as well as the accuracy of the virtual presentation are validated by point cloud comparisons between the CG model and the as‐built structure. Subsequently, the GBDT is used to showcase the evaluation of UAV flight schemes for post‐earthquake inspection of high‐rise buildings. To shorten flight time and place more emphasis on potential damage, FE analysis is conducted to determine the earthquake‐induced damage locations. Consistent damage hotspots are then marked on the CG model, along with restrictions from the real environment such as obstacles, weak satellite signal, wind speed, and lighting conditions considered in the synthetic environment. Finally, applying the synthetic environment as the testbed, three UAV‐aided inspection schemes are implemented virtually and the best UAV flight scheme is determined for the assumed field inspection. This example demonstrates the flexibility of the GBDT in representing the real‐world structure and environmental conditions and its efficacy in assisting decision making for rapid and effective structural inspection in the aftermath of an earthquake.

Research on adaptive shooting algorithm for unmanned aerial vehicles under typical operating conditions of overhead transmission lines

We consider typical environmental conditions, such as different shooting backgrounds, lighting, and shooting distances, and study adaptive shooting algorithms for unmanned aerial vehicle (UAV) on-site environments under typical working conditions to achieve high-quality acquisition of UAV inspection images and reduce the technical difficulty of artificial intelligence recognition of inspection images. Based on the collected high-quality images and the characteristics of unmanned aerial vehicle inspection images, a deep learning technology framework is adopted to study the intelligent identification method of hidden dangers in transmission line equipment and channel environment, achieving the intelligent recognition of pin level fine particle defects.

Optimizing 2D path planning for unmanned aerial vehicle inspection of electric transmission lines

The inspection of electric power transmission lines using unmanned aerial vehicles represents an innovative maintenance approach that several countries, including Morocco, are considering adopting. The primary aim is to enhance existing procedures, capitalize on their advantages, and address challenges associated with traditional methods. Given the precision, efficiency, and safety required for this application to ensure reliable results, we propose a functional architecture for unmanned aerial vehicle-based electric power transmission line inspection. This architecture encompasses key phases such as data collection, planning, and inspection, while identifying electromagnetic interferences and minimum approach distances as major constraints. To streamline mission planning, we introduce a trajectory optimization algorithm integrated into a mission planning assistance application for transmission line inspection. This algorithm presents a novel approach by hybridizing three algorithms: genetic algorithm, ant colony optimization and particle swarm optimization. Consequently, our work represents a significant contribution to the integration of unmanned aerial vehicles in electric power transmission lines inspection operations for electrical energy transport, facilitating potential advancements and discoveries in the field.

A bolt defect detection method for transmission lines based on improved YOLOv5

To solve the problem of bolt defects in unmanned aerial vehicle inspection that are difficult to identify quickly and accurately, this paper proposes a defect detection method based on the improved YOLOv5 anchor mechanism. Firstly, the Normalized Wasserstein distance (NWD) evaluation metric and the Intersection over Union evaluation metric are combined, and the experiment determines the appropriate weight for this combination. This way, the sensitivity of using IoU alone to small objecet detection anchor box threshold changes was reduced. Furthermore, Convolutional Block Attention Module is included into the head network architecture of yolov5 in order to prioritize significant information and suppress irrelevant features. Omni-dimensional Dynamic Convolution (ODConv) is used to replace convolution in MobileNetv2. The combination module is used as the new backbone of the YOLOv5 model. It simultaneously enhances the model’s capability to extract bolt defect object information, minimizes calculation requirements, and achieves lightweight detection across the entire model. Compared with the original algorithm, the model detection Accuracy Precision (AP) is increased by 30.1%, the mean Accuracy Precision is increased by 30.4%. Other evaluation metrics of the model, such as GFlOPs and Parameters, all decreased slightly. The above results show that the improved algorithm proposed in this paper greatly improves the detection accuracy of the model on the premise of ensuring that the model is as small as possible.

Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection

AbstractMotivated by the strengths of unmanned aerial vehicle (UAV), the UAV–human collaboration route planning (UHCRP) for intelligent infrastructure inspection is a problem worthy of discussion to help reduce human costs and minimize the risk of noninspected infrastructures under limited resources. To facilitate UHCRP, this paper proposes a novel deep reinforcement learning (DRL)‐based approach to well handle multi‐source uncertain features and constraints at a fast speed. To begin with, UHCRP is mathematically described and reformulated as a dual interdependent deep reinforcement learning (diDRL) framework to reflect real‐world scenarios. Afterward, a novel policy network named the attention‐based deep neural network (A‐DNN) is introduced to learn the route planning decisions for the combinatorial optimization problem. In particular, A‐DNN is made up of an encoder and a dual decoder for UAV and human inspection, where the multi‐head attention mechanism is incorporated to generate richer representations for model performance improvement. Performance of the proposed dual multi‐head attention model (DAM) has been tested in simulations and a real‐world case study regarding wind farm inspection. Results indicate that DAM under the sampling decoding strategy can deliver a high‐quality path plan and show better generalizability for larger scale problem sizes compared to single‐head attention model (SAM), multi‐head attention model (AM), and two baseline models, namely OR‐Tools and genetic algorithm. Moreover, DAM trained by randomly generated data can be directly employed to solve the practical problem with standardization of inputs. Overall, DRL integrates decision‐making for inspection method selection and inspected infrastructure selection, providing adaptive and intelligent inspection path planning for UAV and human in complex and dynamic engineering environments.

A fault detection method for transmission line components based on synthetic dataset and improved YOLOv5

To address insufficient fault images for transmission line components (TLCs) and low accuracy in fault detection using deep learning techniques, we propose a fault detection method for TLCs based on synthetic datasets and improved YOLOv5. First, we introduce a synthetic image approach that uses prior information from the inspection process of a developed flying-walking power transmission line inspection robot (FPTLIR) to generate a synthetic dataset of fault components (SDFC). Second, we propose an improved YOLOv5 network called CSH-YOLOv5 to improve the accuracy of fault detection. The CSH-YOLOv5 network incorporates the convolutional block attention module (CBAM) and the latest SimCSPSPPF module to improve the detection accuracy of the network. In addition, a statistical analysis of small objects in the SDFC is performed and the neck and head of the YOLOv5 network are optimized accordingly to detect small objects. Finally, to address the lack of fault images in the unmanned aerial vehicle (UAV) inspection dataset, we implement a two-stage transfer learning strategy using the SDFC for training. We then experimentally evaluate the performance of the CSH-YOLOv5 network on a real test dataset. The results show that the CSH-YOLOv5 network achieves a mAP@[0.5] of 98.0% and a mAP@[0.5: 0.95] of 64.6% for fault detection, representing an improvement of 7.6% and 8.1%, respectively, over the YOLOv5 network. Comparative analysis indicates that the CSH-YOLOv5 outperforms other popular object detection networks, including Faster-RCNN, YOLOX, and YOLOv7 networks. The two-stage transfer learning strategy employed significantly enhances the network's generalization ability and detection accuracy on the UAV inspection dataset. The proposed method provides a technical reference for fault detection of TLCs, which can potentially benefit the power transmission industry.

Electric and Magnetic Fields Analysis of the Safety Distance for UAV Inspection around Extra-High Voltage Transmission Lines

The deployment of small unmanned aerial vehicles (UAVs), or drones, for transmission line inspections, has brought attention to the potential impact of electromagnetic fields (EMFs) on UAV operations. This work describes a mathematical model based on the finite elements method (FEM), designed to examine the electric and magnetic fields produced by extra-high voltage (EHV) conductors. The current study extends the analysis to encompass both electric and magnetic fields and evaluates the safe distances for UAVs operating near 345 kV, 500 kV, and 765 kV transmission lines. The electromagnetic environment around these EHV transmission lines was simulated using electrostatic, magnetostatic, and transient magnetic modules within the QuickField software 6.6. Electric and magnetic profiles were estimated using 2D finite element analysis, including a numerical simulation for phase-to-phase fault EMFs for the above transmission lines. These results were then cross-verified with theoretical calculations at specific intervals and further validated using the EMFACDC analytical method developed by the International Telecommunication Union. This comprehensive assessment concludes that precise distance considerations are necessary to ensure UAV safety during power line inspections, mitigating potential risks from EMF interference.

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities.

An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post‐disaster infrastructures

AbstractCatastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution r. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed‐loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

AbstractIntelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

A rapid unmanned aerial vehicle inspection path planning method based on hybrid heuristic algorithm

The advancement of unmanned aerial vehicle (UAV) and remote sensing technologies has fueled interest in automatic UAV inspection path planning based on inspection tasks. However, traditional methods suffer from limitations such as manual operation, inability to find optimized paths, and lengthy time consumption. We propose a rapid UAV inspection path planning method using a hybrid heuristic algorithm in this paper. First, real-world data is abstracted into a graph. Then, a hybrid heuristic algorithm is proposed and used to determine an optimal inspection path considering the tasks and power parameters of the UAV. Finally, the total power consumption for the mission is computed to assess UAV battery capacity adequacy. Experimental results demonstrate the effectiveness of our method in terms of both performance and accuracy.

Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos

The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3[Formula: see text] image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3[Formula: see text] algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1[Formula: see text]ms and 13.2[Formula: see text]ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7[Formula: see text]ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7[Formula: see text]ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly improved the accuracy and efficiency of intelligent inspection of substation meter readings.