In recent years, unmanned aerial vehicles (UAV) have been increasingly used in power line inspections. Birds often nest on transmission line towers, which threatens safe power line operation. The existing research on bird’s nest inspection using UAVs mainly stays at the level of image postprocessing detection, which has poor real-time performance and cannot obtain timely bird’s nest detection results. Considering the above shortcomings, we designed a power inspection UAV system based on deep learning technology for autonomous flight, positioning and photography, real-time bird nest detection, and result export. In this research, 2000 bird’s nest images in the actual power inspection environment were shot and collected to create the dataset. The parameter optimization and test comparison for bird’s nest detection are based on the three target detection models of YOLOv3, YOLOv5-s, and YOLOX-s. A YOLOv5-s bird’s nest detection model optimized for bird’s nest real-time detection is proposed, and it is deployed to the onboard computer for real-time detection and verification during flight. The DJI M300 RTK UAV was used to conduct a test flight in a natural power inspection environment. The test results show that the mAP of the UAV system designed in this paper for bird’s nest detection is 92.1%, and the real-time detection frame rate is 33.9 FPS. Compared with the previous research results, this paper proposes a new practice of using drones for bird’s nest detection, dramatically improving the real-time accuracy of bird’s nest detection. The UAV system can efficiently complete the task of bird’s nest detection in the process of electric power inspection, which can significantly reduce manpower consumption in the power inspection process.

Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.

Fixed-wing unmanned aerial vehicles (UAVs) have the advantages of long endurance and fast flight speed and are widely used in surveying, mapping, monitoring, and defense fields. However, its conventional take-off and landing methods require runway support. Achieving runway-free recovery is necessary for expanding the application of fixed-wing UAVs. This research comprehensively reviews the various techniques and scenarios of runway-free recovery of fixed-wing UAVs and summarizes the key technologies. The above methods cover parachute recovery, net recovery, rope recovery, SideArm recovery, deep stall recovery, towed drogue docking recovery, and robotic arm recovery methods within runway-free recovery. Finally, this research discusses the future research directions of runway-free recovery.

A new digital measurement system is presented in this manuscript. A wearable smart measurement system equipped with monocular augmented reality (AR) glasses and Wi-Fi and Bluetooth modules is designed, which is more suitable for high-altitude measurement occasions than traditional digital multimeters (DMM). The measurement data could be displayed on AR glasses supported by Bluetooth module in real time. Also, the effective management of large-scale industrial measurement data could be implemented through the IoT data interaction platform with high speed communication by employing a Wi-Fi module. In addition, the wire-receiving device is introduced into the system, which effectively solves the problem that occurs when the traditional DMM harness is tangled. Hall sensors are adopted to measure the current and solves the problem that the traditional DMM needs to be connected into an electronic circuit in series.

Formulating a course of action (COA) before a combat is crucial for operational command. Research in command and control (C2) artificial intelligence is currently focused on using intelligent auxiliary decision-making methods to implement COA. This paper proposes a COA planning method based on line of operation (LOO) and uses planning domain definition language (PDDL) to describe combat scenarios and COA. Following the effect-based optimization (EBO) principle, an effect evaluation model for COA was constructed, and dynamic bayesian networks (DBNs) was used to determine the reasoning and calculate the results of the effect evaluation network. To further improve the execution efficiency of the effect evaluation model in practical applications, the network was optimized through a back propagation neural network (BPNN). Relevant experiments based on the coordinated distributed air defense and anti-missile scenario were carried out using the LOO model to complete the planning of COA. A BPNN evaluation model based on the DBNs evaluation model was built. After training and fine-tuning, it achieved similar evaluation results, with a mean absolute percentage error (MAPE) of less than 0.02%. Compared with the DBNs model, the BPNN model achieved an efficiency improvement of no less than 65%, effectively reducing the consumption of computing resources. This research is the first time to realize the modeled description of COA planning, automatic evaluation, and calculation optimization of COA effects. It can support the development of decision support systems (DSS) and has the potential for practical application.

A new structure and an improved control method of the three‐axis gimbal for hexacopter unmanned aerial vehicle (UAV) are presented. By optimising the structure of the three‐axis gimbal and establishing the mathematical model with the maximum stability, the improved structure of three‐axis gimbal is proposed, which two groups of virtual reality (VR) camera arrays are mounted on the top and bottom of the frame to meet requirements of the 720‐degree panoramic shot. The redundant design of five motors in the structure could realise the stabilisation of three‐axis linkage of the gimbal that will ensure the optimal performance of the independent stable cameras all the time. At the same time, the attitude of the gimbal could be controlled by the complementary filter and proportional, integral and derivative algorithms. Furthermore, the closed‐loop controls of angle, speed and current of Brushless DC motor are realised by the non‐contact magnetic encoder, which improves the anti‐shake performance of the system. The experiments in the prototype are designed to prove the anti‐shake performance of the proposed gimbal and stability of the hexacopter UAV.