Unmanned Aerial Vehicle Target Research Articles

Enhancing Target Tracking: A Novel Grid-Based Beetle Antennae Search Algorithm and Confusion-Aware Detection.

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm's rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system's robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model's high discriminative ability. The results also indicate that the model meets the real-time requirements.

UAV networks DoS attacks detection using artificial intelligence based on weighted machine learning

While Unmanned Aerial Vehicles (UAVs) have found applications across numerous industries, they still remain vulnerable to various cybersecurity challenges. Different types of cyberattacks target UAVs. Early detection of these cyberattacks is considered the most important step in ensuring the cybersecurity of UAVs. In this article, an artificial intelligence method based on machine learning was developed for detecting different types of Denial of Service (DoS) attacks targeting the UAV network. Initially in this work, feature selection methods are implemented to select the most important features. Then, machine learning methods are used to classify attacks. According to the conducted experiments, the proposed method outperformed others with an accuracy of 99.51 % and a prediction time of 0.1 s. Additionally, a novel dataset is used in this work, which offers several advantages. The dataset was created within a real-world environment rather than a simulated one. Furthermore, the data were collected within a 5G network.

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Realizing Small UAV Targets Recognition via Multi-Dimensional Feature Fusion of High-Resolution Radar

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets.

Improved faster region convolutional neural network algorithm for UAV target detection in complex environment

With the development of artificial intelligence technology and Unmanned Aerial Vehicle (UAV) technology, the traditional UAV target detection methods are difficult to achieve high detection accuracy in complex and changeable scenes. The paper selects Faster Region Convolutional Neural Network (Faster R-CNN) as the basic algorithm due to its high scalability and excellent classification performance. The convolution mode of the convolution layer in the network structure is adjusted to deformable convolution. Additionally, a hybrid attention mechanism module is added to combine channel attention and spatial attention modules behind the output layer of the Faster R-CNN network. Finally, the Soft Non-Maximum Suppression (Soft-NMS) method is selected to combine the improved Faster R-CNN algorithm of multiple individuals into the final target detection model. The performance of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm was verified through the VisDrone 2018-DET dataset and the full class average accuracy Mean Average Precision (mAP), accuracy and precision. The accuracy and logarithm of loss values of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm's Region Proposal Network (RPN) were 0.985, 0.981, 0.018, and 0.052, respectively. The target detection model based on the hybrid attention mechanism of ResNet-50 network fusion Spartial Attention Module (SAM) and Channel Attention Module (CAM) had the best classification performance, with the accuracy, precision and overall average accuracy of 25.8 %, 24.6 % and 22.7 %, respectively. The results are helpful to improve the target detection ability of UAV in complex environment, and contribute to the development of target detection technology in the future.

A Dynamic Visual SLAM System Incorporating Object Tracking for UAVs

The capability of unmanned aerial vehicles (UAVs) to capture and utilize dynamic object information assumes critical significance for decision making and scene understanding. This paper presents a method for UAV relative positioning and target tracking based on a visual simultaneousocalization and mapping (SLAM) framework. By integrating an object detection neural network into the SLAM framework, this method can detect moving objects and effectively reconstruct the 3D map of the environment from image sequences. For multiple object tracking tasks, we combine the region matching of semantic detection boxes and the point matching of the optical flow method to perform dynamic object association. This joint association strategy can prevent trackingoss due to the small proportion of the object in the whole image sequence. To address the problem ofacking scale information in the visual SLAM system, we recover the altitude data based on a RANSAC-based plane estimation approach. The proposed method is tested on both the self-created UAV dataset and the KITTI dataset to evaluate its performance. The results demonstrate the robustness and effectiveness of the solution in facilitating UAV flights.

Unmanned Aerial Vehicle Fire Detection Platform Based on Semantic Yolov5 and Autonomous Recognition

In recent years, as fire image detection has become a research hotspot. One class of methods is color-based methods, which are very sensitive to brightness and shadows. As a result, the number of false alarms generated by these methods is high. Aiming at the task requirements of airborne binocular vision obstacle avoidance and target tracking, this paper establishes the verification platform architecture of UAV (Unmanned Aerial Vehicle) binocular vision obstacle avoidance and target tracking. For the update and maintenance of boundary regions, we can also continuously extract richer information from the boundary, make more elaborate plans, and develop an incremental method to detect locally updated maps within the boundary. The fire point can be independently and quickly identified through deep learning to extinguish the fire accurately. Assuming that the system incorrectly identifies 2 out of 80 non-fire sources as fire sources, so the results indicate a precision of about 88%, a recall of 90%. However, the traditional fire detection is around 80%.

Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confronted with UAV aerial images compared to general scenes. This paper proposes an outstanding small target detection algorithm for UAVs, named Fine-Grained Feature Perception YOLOv8s-P2 (FGFP-YOLOv8s-P2), based on YOLOv8s-P2 architecture. We specialize in improving inspection accuracy while meeting real-time inspection requirements. First, we enhance the targets’ pixel information by utilizing slice-assisted training and inference techniques, thereby reducing missed detections. Then, we propose a feature extraction module with deformable convolutions. Decoupling the learning process of offset and modulation scalar enables better adaptation to variations in the size and shape of diverse targets. In addition, we introduce a large kernel spatial pyramid pooling module. By cascading convolutions, we leverage the advantages of large kernels to flexibly adjust the model’s attention to various regions of high-level feature maps, better adapting to complex visual scenes and circumventing the cost drawbacks associated with large kernels. To match the excellent real-time detection performance of the baseline model, we propose an improved Random FasterNet Block. This block introduces randomness during convolution and captures spatial features of non-linear transformation channels, enriching feature representations and enhancing model efficiency. Extensive experiments and comprehensive evaluations on the VisDrone2019 and DOTA-v1.0 datasets demonstrate the effectiveness of FGFP-YOLOv8s-P2. This achievement provides robust technical support for efficient small target detection by UAVs in complex scenarios.

Web-enhanced unmanned aerial vehicle target search method combining imitation learning and reinforcement learning

PurposeThis paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.Design/methodology/approachFirstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.FindingsThe method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.Originality/valueThe agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

Web intelligence-enhanced unmanned aerial vehicle target search model based on reinforcement learning for cooperative tasks

PurposeBecause of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.Design/methodology/approachThis study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.FindingsThis study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.Originality/valueIt is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

A deep neural network approach with attention mechanism to improve the quality of target observation for UAVs

Owing to the limitations of onboard equipment, the direct acquisition of high-resolution images in unmanned aerial vehicle (UAV) target observation tasks is a challenge. However, with the development of deep learning, super-resolution reconstruction has become a popular technique for enhancing image resolution. Though, current super-resolution reconstruction methods can enhance UAV target observation images to some extent, most of them are unable to strike a balance between the number of parameters, computational cost, and reconstruction effect. Furthermore, limited research has been conducted for overcoming the cascade coupling problem in UAV target observation images. To solve these problems and provide a lightweight, fast, and high-quality super-resolution method for UAV target observation images, an enhanced attention dense cross network (EADCN) is proposed. This method includes excellent attention mechanisms, such as the double large kernel attention module (D-LKA) and multi-aware channel attention distillation block (MAADB). In addition to benchmark datasets, a UAV target observation dataset (UAV-TOD) is created to evaluate the performance of EADCN. The experimental results demonstrate that the EADCN has fewer parameters, low computational complexity, high accuracy, and fast processing speed, making it suitable for deployment in UAVs.

Technological Innovation: New Unmanned Aerial Vehicle Target Tracking Technology

This article mainly discusses a new type of unmanned aerial vehicle target tracking technology, comprising a drone using a built-in GPS module and a device with a built-in GPS module worn by a human body. When the human body is in motion, the device worn by the human body and the drone generate pseudocodes; Perform pseudorange measurement at a certain moment in GPS time; Obtain a nonlinear equation system related to pseudorange, repeat iterative calculations to obtain accurate coordinates, use Dijkstra algorithm to calculate the shortest path in a weighted directed graph, and the drone flies according to the shortest movement trajectory from the starting point to the human target point; The drone and device of the present invention perform pseudorange measurement at a certain moment in GPS time, and repeat iterative calculations to reduce errors, which is conducive to obtaining accurate coordinates of the device and drone; Based on weighted graph search, the coordinates of the drone and the human body are treated as two endpoints, representing the starting and ending points. The algorithm calculates the shortest trajectory in the graph.

UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking.

UV communication cooperative formation UAV alliance capture algorithm.

The capture of target unmanned aerial vehicles (UAVs) by a UAV formation is one of the important and typical tasks in multi-UAV battlefield operations. In this paper, an ultraviolet (UV) light communication-assisted formation UAV alliance capture algorithm is proposed, which combines UV light communication technology with a capture algorithm. With the help of wireless UV light to assist UAV formation inter-UAV data confidentiality transmission and non-line-of-sight communication, the algorithm integrates the alliance generation algorithm with the region minimization strategy, solves the optimal alliance structure by using the dynamic programming method, and implements the aerial capture of the target UAVs by using the region minimization strategy, so as to complete the task of efficiently capturing multi-targets by the UAV formation in complex scenarios. Simulation comparisons were conducted between the region minimization strategy and the proposed UV communication-assisted formation UAV alliance capture algorithm. The results show that the proposed algorithm reduces energy consumption by 12.73% on average and decreases the average number of algorithm iterations by 27.49% during the UAV formation capture of multiple targets, which verifies its low energy consumption and high capture efficiency.

Lightweight air-to-air unmanned aerial vehicle target detection model

The rapid expansion of the drone industry has resulted in a substantial increase in the number of low-altitude drones, giving rise to concerns regarding collision avoidance and countermeasure strategies among these unmanned aerial vehicles. These challenges underscore the urgent need for air-to-air drone target detection. An effective target detection model must exhibit high accuracy, real-time capabilities, and a lightweight network architecture to achieve a balance between precision and speed when deployed on embedded devices. In response to these requirements, we initially curated a dataset comprising over 10,000 images of low-altitude operating drones. This dataset encompasses diverse and intricate backgrounds, significantly enhancing the model’s training capacity. Subsequently, a series of enhancements were applied to the YOLOv5 algorithm to realize lightweight object detection. A novel feature extraction network, CF2-MC, streamlined the feature extraction process, while an innovative module, MG, in the feature fusion section aimed to improve detection accuracy and reduce model complexity. Concurrently, the original CIoU loss function was replaced with the EIoU loss function to further augment the model’s accuracy. Experimental results demonstrate an enhancement in the accuracy of drone target detection, achieving mAP values of 95.4% on the UAVfly dataset and 82.2% on the Det-Fly dataset. Finally, real-world testing conducted on the Jetson TX2 revealed that the YOLOv5s-ngn model achieved an average inference speed of 14.5 milliseconds per image. The code utilized in this paper can be accessed via https://github.com/lucien22588/yolov5-ngn.git.

Framework for Autonomous UAV Navigation and Target Detection in Global-Navigation-Satellite-System-Denied and Visually Degraded Environments

Autonomous Unmanned Aerial Vehicles (UAVs) have possible applications in wildlife monitoring, disaster monitoring, and emergency Search and Rescue (SAR). Autonomous capabilities such as waypoint flight modes and obstacle avoidance, as well as their ability to survey large areas, make UAVs the prime choice for these critical applications. However, autonomous UAVs usually rely on the Global Navigation Satellite System (GNSS) for navigation and normal visibility conditions to obtain observations and data on their surrounding environment. These two parameters are often lacking due to the challenging conditions in which these critical applications can take place, limiting the range of utilisation of autonomous UAVs. This paper presents a framework enabling a UAV to autonomously navigate and detect targets in GNSS-denied and visually degraded environments. The navigation and target detection problem is formulated as an autonomous Sequential Decision Problem (SDP) with uncertainty caused by the lack of the GNSS and low visibility. The SDP is modelled as a Partially Observable Markov Decision Process (POMDP) and tested using the Adaptive Belief Tree (ABT) algorithm. The framework is tested in simulations and real life using a navigation task based on a classic SAR operation in a cluttered indoor environment with different visibility conditions. The framework is composed of a small UAV with a weight of 5 kg, a thermal camera used for target detection, and an onboard computer running all the computationally intensive tasks. The results of this study show the robustness of the proposed framework to autonomously explore and detect targets using thermal imagery under different visibility conditions. Devising UAVs that are capable of navigating in challenging environments with degraded visibility can encourage authorities and public institutions to consider the use of autonomous remote platforms to locate stranded people in disaster scenarios.

Equivalent Modeling and Analysis of Handover Process in K-Tier UAV Networks

Featured by flexible deployment, unmanned aerial vehicles (UAVs) can form multi-tier networks in 3D space, enhancing the capacity, but this heterogeneity causes more unnecessary handovers (HOs), i.e., ping-pongs (PPs) and handover failures (HOFs). However, existing studies have not modeled the HO process in multi-tier UAV networks. This paper proposes an equivalent model for the HO process in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -tier UAV networks, where nonlinear mapping rules are established from the altitude and transmit power to equivalent horizontal distances specific to HO events, then HO status during time-to-trigger (TTT) is analyzed via the mapping rules. Specifically, each trigger condition of the HO events is transformed into a circular boundary centered on the projection of the target UAV with the corresponding trigger radii. Considering the practical HO process, the HO trigger location is tractably modeled as a random variable of the path length from the start, and the signal-to-interference ratio during TTT and the residence time are analyzed through equivalent geometric relations. The results show that there exists an altitude ratio that causes the most frequent HO, which increases by 25% to 150% under different configurations. Trade-offs between HOF and PP exist in the altitude ratio and transmit power ratio, besides the HO parameters.

Infrared UAV Target Detection Based on Continuous-Coupled Neural Network.

The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image's standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection.

CPA-YOLOv7: Contextual and pyramid attention-based improvement of YOLOv7 for drones scene target detection

Target detection in unmanned aerial vehicle application scenarios has other problems, such as dense targets. The existing unmanned aerial vehicle target detection model with high computational complexity makes it difficult to meet real-time unmanned aerial vehicle target detection, and the detection accuracy of small targets is low. To address these problems, we propose an improved YOLOv7 small target detection model based on context and pyramidal attention that can cope with dense unmanned aerial vehicle scenarios - CPA-YOLOv7. This model embeds our proposed lightweight multi-scale attentional feature spatial pyramid pooling module, which can better distinguish between small and large target features, reducing the computational effort while improving the detection accuracy of the model. Secondly, we design a contextual dynamic fusion attention module in the network to fuse global and local contextual information and dynamically assign features to multiple groups of channels; in the multi-scale fusion process, it effectively increases the characterization ability of small target features and enables the network to better focus on small target information. Finally, we improve Wise-Intersection-over-Union loss as the regression loss function, add a moderation factor to retain some of the high and low-quality sample weights to improve the regression accuracy of high-quality anchor frames, and use the dynamic non-monotonic focusing mechanism to increase the model's focus on ordinary quality anchor frames to improve the model's localization performance and robustness to low-quality samples. Numerous experimental results show that on the unmanned aerial vehicle datasets VisDrone2021-DET and AI-TOD, the mAP values of our model are 2.3% and 1.1% higher than those of the YOLOv7 model with fewer parameters introduced, and the computational speed reaches 146 frames per second (FPS), which can meet the real-time requirements of unmanned aerial vehicle detection.

UAV Target Detection for IoT via Enhancing ERP Component by Brain–Computer Interface System

The increasing popularity of Internet of Things (IoT) devices provides a huge data source for intelligent identification. Images captured by unmanned aerial vehicle (UAV) are often exploited in target detection missions for search, rescue and fire prevention. However, without sufficient training samples, the machine learning method is usually difficult to meet application requirements. To solve the problem, a brain-computer interface (BCI) real-time system is applied to UAV target detection. In this study, a novel rapid serial visual presentation (RSVP) paradigm was formulated to present images, enhancing the ability for wide-area target detection. Since there is spatial correlation in captured images, joint decision for identical target can improve the recognition efficiency. To suppress interfering components and improve event-related potential (ERP) detection efficiency, an enhancing ERP component (EEC) algorithm is proposed. Both decision method and EEC algorithm are based on strictly statistical theory. RSVP task was performed by 12 subjects. The interference components and noise correlation were significantly reduced by EEC algorithm. The target detection rate online was 86.6% while the false alarm rate was less than 5%. Besides, the joint decision strategy raised the area under curve (AUC) value from 0.876 to 0.963. The proposed BCI real-time system realizes the complementarity of human intelligence and IoT, ushering UAV target detection into the era of hybrid intelligence.