Millimeter-Wave Radar Target Detection Based on Inter-frame DBSCAN Clustering

Vehicle detection is an important technology in au-tonomous driving, for which high detection accuracy and real-time performance are often required. The YOLOv5-GE vehicle detection algorithm is proposed to address the situation that the YOLOv5 vehicle detection model has false detection and missed detection for small and dense targets in complex environments. The global attention mechanism is added to the backbone net-work of the YOLOOv5 model, which is composed of two inde-pendent submodules of channel attention and convolutional spa-tial attention, which prevents the loss of information to a certain extent and amplifies the interaction of global dimensions. Second-ly, the training process is optimized using the Focal-EloU loss function to replace the GloU loss function, which improves the accuracy of vehicle detection. Finally, the proposed YOLOv5-GE algorithm and the YOLOv5 algorithm are subjected to a con-trolled experiment on the KITTI dataset. The experimental re-sults show that the YOLOv5-GE algorithm achieves an average accuracy of 86% while maintaining real-time performance, which is 2.5% higher than that of the YOLOv5 algorithm, and can im-prove the detection accuracy of small and dense targets in com-plex environments.

In the task of using robots to capture end-of-life cars, the position of the vehicle door frame needs to be grasped. A fast and accurate positioning of the vehicle door frame is the key to realizing the automatic car grasping process. Traditional methods for locating and grasping scrap cars rely heavily on manual operations and suffer from low grasping efficiency and poor accuracy. Therefore, this paper proposes a binocular vision robot vehicle door frame spatial localization method based on the improved YOLOv4. This method includes a lightweight and efficient feature fusion target detection network in complex environments, and the target detection results are combined with an enhanced SURF feature–matching method to locate the vehicle door frame position. To simplify the network structure, MobileNetv3 is used instead of the backbone network CSPDarknet53, and deep separable convolution is used in the network. To increase the sensitivity of the network to vehicle door frame targets in complex environments, an improved convolutional block attention module is added to the pyramid attention with simple network backbones. Moreover, adaptive spatial feature fusion is introduced into the network to fully use the features at different scales for more effective feature fusion. Compared with YOLOv4, the number of network parameters is reduced by 73.8%, the mAP is improved by 1.35%, and the detection speed is increased by 28.7%. The experimental results demonstrate that the positioning accuracy of the system is 0.745 mm, which meets the positioning measurement error of less than 1 cm required for the vehicle door frame. The paper also compares our findings with other network models. The results show that the method achieves a good balance between detection speed and detection accuracy, satisfying the task of identifying vehicle door frames in complex environments with good detection results.

Target detection and tracking represent key challenges facing miniature fixed-wing unmanned aerial vehicles (UAVs), particularly at high cruising speeds. Therefore, this paper proposes a vision-based target detection and tracking algorithm that systematically couples two mainstream methods, namely, you only look once (YOLO) and kernel correlation filter (KCF) algorithms. This combination enables small fixed-wing UAVs to achieve reliable target detection and rapid target tracking. A customized vision-guidance module is constructed to implement this algorithm, and a dual-thread execution mechanism is developed to ensure that the computational resources are used effectively. A miniature fixed-wing UAV experimental platform is also constructed and evaluated. Flight experiments are performed, and the results demonstrate that the developed algorithm can achieve satisfactory detection and tracking accuracy for stationary and moving ground targets in complex environments.

To meet the urgent demand of low-observable moving target detection in complex environments, a novel method of Frequency Diverse Array (FDA) radar signal processing method based on Space-Rang-Doppler Focusing (SRDF) is proposed in this paper. The current development status of the FDA radar, the design of the array structure, beamforming, and joint estimation of distance and angle are systematically reviewed. The extra degrees of freedom provided by FDA radar are fully utilizsed, which include the Degrees Of Freedom (DOFs) of the transmitted waveform, the location of array elements, correlation of beam azimuth and distance, and the long dwell time, which are also the DOFs in joint spatial (angle), distance, and frequency (Doppler) dimensions. Simulation results show that the proposed method has the potential of improving target detection and parameter estimation for weak moving targets in complex environments and has broad application prospects in clutter and interference suppression, moving target refinement, etc..

针对图像序列中运动目标检测、跟踪的难点问题，提出了一种实时运动目标检测与跟踪算法。该算法基于自适应背景建模，获取运动目标背景模型和前景图像，从而实现运动目标检测；通过建立运动目标的位置、大小、形状以及颜色分布模型，构造运动目标全局匹配函数，结合目标活力特征，实现多运动目标连续匹配和跟踪。实验结果表明，相对于传统的运动目标跟踪方法，本文方法明显减少了运算时间，增强了环境适应性，实现了复杂场景下运动目标的准确检测和稳定跟踪，对非刚性目标的形变、旋转具有较强的鲁棒性。In this paper, we propose a real-time mobile target detection and tracking algorithm for challenges of mobile target detection and tracking in sequential images. This algorithm based on the adaptive background modeling obtains background model and front-view images of mobile targets, which is the way to achieve target detection. Continuous matching and tracking of multiple mobile targets are realized through constructing position, size, shape and color distribution of the mobile targets, defining a global matching function for those targets, and associating their vitality characteristics. It is demonstrated by experiments that the algorithm presented in this paper, compared to the traditional methods of mobile target tracking, significantly reduces the computation time, improves adaptive feature to environments, achieves accurate detection and robust tracking of mobile targets in complex environments, and shows strong robustness to deformation and rotation of non-rigid targets.

Both visible and infrared images are important sources of intelligence information on the battlefield, and air‐to‐ground reconnaissance by UAV is an important means to obtain intelligence. However, there are great challenges in ground target detection and tracking, especially in complex battlefield environments. Aiming at the problem of insufficient accuracy of target detection by a single type of sensor in the battlefield environment at this stage, a target detection method by fusion of visible and infrared images is proposed in this paper, which is called ReconnaissanceFusion‐YOLO (RF‐YOLO), and with the help of infrared imagery, it can effectively improve the accuracy of target detection in the case of insufficient light. The performance of target detection in the battlefield is significantly improved by introducing two key innovative modules: dual feature fusion (DFF) module and feature fusion corrector (FFC) module. The DFF module enhances multi‐channel feature fusion through a novel concatenation and channel‐wise attention mechanism, while the FFC module performs feature correction between parallel streams using spatial and channel‐wise weights, addressing noise and uncertainty in different modalities. These modules are integrated on top of a dual‐stream YOLO architecture, allowing for effective fusion of visible and infrared information. RF‐YOLO was trained and evaluated using the FLIR data set, containing 5142 pairs of strictly aligned visible and infrared images. Results demonstrate that RF‐YOLO significantly outperforms benchmark networks in terms of robustness requirements. Specifically, the large model of RF‐YOLO achieves an mAP of 0.831, which is a significant improvement compared to the YOLOv5l inf benchmark's 0.739. This represents an improvement of over 12% in detection accuracy. Additionally, RF‐YOLO offers a Nano version that balances accuracy and speed. The Nano version achieves an mAP of 0.765, while maintaining a model size of only 11.5 MB, making it suitable for deployment on UAV edge computing devices with limited resources. To validate the practical applicability of our approach, this paper successfully implements target detection and tracking on a real UAV's edge computing device using the ROS system and SiameseRPN, combined with the proposed RF‐YOLO. Real‐world flight tests were conducted on an internal playground, demonstrating the effectiveness of our method in actual UAV applications. The system achieved a processing rate of approximately 10 fps at 640 × 640 resolution on an NVIDIA TX2 edge computing device, showcasing its real‐time performance capability in practical scenarios. This study contributes to enhancing UAV‐based battlefield reconnaissance capabilities by improving the accuracy and robustness of target detection and tracking in complex environments. The proposed RF‐YOLO method, along with its successful implementation on a real UAV platform, provides a promising solution for advanced military intelligence gathering and decision‐making support.

Objective: In the task of camouflage target detection, there is a problem that the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A target detection algorithm CM-YOLOv5s is proposed for camouflage characteristics. Method: The algorithm uses YOLOv5s as the basic framework. First, a coordinated attention mechanism is embedded in the backbone feature extraction network, which enhances the network’s ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm’s anti-background. Interference ability; secondly, the Mixup data enhancement strategy is used to simulate overlapping occlusion scenarios, which further strengthens the network model’s learning ability for complex samples. Results: The training and verification were carried out on the self-made Military Camouflage Target Dataset (MCTD), and the precision, recall, and average mAP of the improved CM-YOLOv5s algorithm reached 95.9%, 87.1%, and 94.1, respectively. %, compared with the original YOLOv5s model, the average accuracy rate is improved by 3.8 percentage points. Conclusion: The improved algorithm has better detection effect, and realizes accurate identification and rapid positioning of military camouflage targets in complex environments.

Ultrawideband (UWB) radar technology has emerged as one of the preferred choices for through-wall detection due to its high range resolution and good penetration. The resolution is a result of high bandwidth of UWB radar and helpful for better separation of multiple targets in complex environment. Detection of human targets through a wall is interesting in many applications. One significant characteristic of human is the periodic motion, such as breathing and limb movement. In this letter, we apply the UWB radar system in through-wall human detection and present the methods based on fast Fourier transform and S transform to detect and identify the human's life characteristic. In particular, we can extract the center frequencies of life signals and locate the position of human targets from experimental data with high accuracy. Compared with other research studies in through-wall detection, this letter is concentrated in the processing and identifying of the life signal under strong clutter. It has a high signal-to-noise ratio and simpler to implement in complex environment detection. We can use the method to search and locate the survivor trapped under the building debris during earthquake, explosion, or fire.

A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

A new method is presented for visualizing data as they are generated from real-time applications. These techniques allow viewers to perform simple data analysis tasks such as detection of data groups and boundaries, target detection, and estimation. The goal is to do this rapidly and accurately on a dynamic sequence of data frames. Our techniques take advantage of an ability of the human visual system called preattentive processing. Preattentive processing refers to an initial organization of the visual system based on operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, orientation, intensity, size, curvature, and line length. We believe that studies from preattentive processing should be used to assist in the design of visualization tools, especially those for which high speed target, boundary, and region detection are important. Previous work has shown that results from research in preattentive processing can be used to build visualization tools that allow rapid and accurate analysis of individual, static data frames. We extend these techniques to a dynamic real-time environment. This allows users to perform similar tasks on dynamic sequences of frames, exactly like those generated by real-time systems such as visual interactive simulation. We studied two known preattentive features, hue and curvature. The primary question investigated was whether rapid and accurate target and boundary detection in dynamic sequences is possible using these features. Behavioral experiments were run that simulated displays from our preattentive visualization tools. Analysis of the results of the experiments showed that rapid and accurate target and boundary detection is possible with both hue and curvature. A second question, whether interactions occur between the two features in a real-time environment, was answered positively.

The detection and tracking of dim targets in complex environments show great importance in radar systems. The track-before-detect (TBD) technology has been widely researched in the scenario where the target signal-to-noise ratio (SNR) is low, however, there has been no suitable solution to the trade-off between tracking accuracy and computational complexity during the process of multi-frame joint detection in radar systems. In this paper, we propose an efficient two-stage based multi-frame detection and tracking algorithm in radar systems. The proposed algorithm consists of a low threshold pre-processing stage, and a TBD processor, which searches possible target tracks from multiple scans and declares the final estimated tracks. The proposed algorithm provides an accurate evolution of target states over time in polar coordinates to avoid the performance loss and model mismatch due to the nonlinear conversion in mixed coordinates. In addition, we further propose a greedy-based recursive algorithm to implement fast track formation from the over-threshold multi-frame measurement points. Simulation results show that the proposed method achieves a better detection and tracking performance with a low computational complexity.

To enhance the recognition of camouflaged targets in complex environments, this work employs polarization hyperspectral imaging to systematically examine the spectral response characteristics of four representative camouflage materials across multiple polarization channels. The contribution of each polarization channel (S1, S2, DOLP, and AOLP) is quantified relative to the non-polarized baseline using four spectral dissimilarity metrics: spectral angle (SA), root-mean-square error, spectral information divergence, and the red-edge spectral difference index. The results demonstrate that polarization information effectively enhances the spectral distinction between the camouflage materials and the grassland background, with the angle of linear polarization (AOLP) and the polarization difference channels (S1 and S2) exhibiting the most prominent effects. Based on these findings, channel fusion strategies centered on AOLP, S1, and S2 are proposed for different materials, and quantitative thresholds are established to improve the accuracy of practical camouflage target identification. This work provides a theoretical basis and methodological reference for the real-world application of polarization hyperspectral imaging in camouflage target detection.

Accurate bud detection is a prerequisite for automatic tea picking and yield statistics; however, current research suffers from missed detection due to the variety of singleness and false detection under complex backgrounds. Traditional target detection models are mainly based on CNN, but CNN can only achieve the extraction of local feature information, which is a lack of advantages for the accurate identification of targets in complex environments, and Transformer can be a good solution to the problem. Therefore, based on a multi-variety tea bud dataset, this study proposes RT-DETR-Tea, an improved object detection model under the real-time detection Transformer (RT-DETR) framework. This model uses cascaded group attention to replace the multi-head self-attention (MHSA) mechanism in the attention-based intra-scale feature interaction (AIFI) module, effectively optimizing deep features and enriching the semantic information of features. The original cross-scale feature-fusion module (CCFM) mechanism is improved to establish the gather-and-distribute-Tea (GD-Tea) mechanism for multi-level feature fusion, which can effectively fuse low-level and high-level semantic information and large and small tea bud features in natural environments. The submodule of DilatedReparamBlock in UniRepLKNet was employed to improve RepC3 to achieve an efficient fusion of tea bud feature information and ensure the accuracy of the detection head. Ablation experiments show that the precision and mean average precision of the proposed RT-DETR-Tea model are 96.1% and 79.7%, respectively, which are increased by 5.2% and 2.4% compared to those of the original model, indicating the model’s effectiveness. The model also shows good detection performance on the newly constructed tea bud dataset. Compared with other detection algorithms, the improved RT-DETR-Tea model demonstrates superior tea bud detection performance, providing effective technical support for smart tea garden management and production.

MXene-based thermal camouflage materials have gained increasing attention due to their low emissivity, however, the poor anti-oxidation restricts their potential applications under complex environments. Various modification methods and strategies, e.g., the addition of antioxidant molecules and fillers have been developed to overcome this, but the realization of long-term, reliable thermal camouflage using MXene network (coating) with excellent comprehensive performance remains a great challenge. Here, a MXene-based hybrid network comodified with hyaluronic acid (HA) and hyperbranched polysiloxane (HSi) molecules is designed and fabricated. Notably, the presence of appreciated HA molecules restricts the oxidation of MXene sheets without altering infrared stealth performance, superior to other water-soluble polymers; while the HSi molecules can act as efficient cross-linking agents to generate strong interactions between MXene sheets and HA molecules. The optimized MXene/HA/HSi composites exhibit excellent mechanical flexibility (folded into crane structure), good water/solvent resistance, and long-term stable thermal camouflage capability (with low infrared emissivity of ≈0.29). The long-term thermal camouflage reliability (≈8 months) under various outdoor weathers and the scalable coating capability of the MXene-coated textile enable them to disguise the IR signal of various targets in complex environments, indicating the great promise of achieved material for thermal camouflage, IR stealth, and counter surveillance.

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