Multi-target Detection Algorithm Research Articles

3-D Dynamic Multitarget Detection Algorithm Based on Cross-View Feature Fusion

3-D Dynamic Multitarget Detection Algorithm Based on Cross-View Feature Fusion

A multi-target detection and position tracking algorithm based on mmWave-FMCW radar data

Detecting and tracking the position of multiple targets indoors is a challenging measurement problem due to the inherent difficulty to cluster correctly the sensor data associated to a given target and to track the position of each cluster with adequate accuracy. This problem is critical especially in rooms filled with fixed or moving objects hampering target detection and whenever the paths of different targets cross one another. In this paper, a robust Multiple Targets Tracking (MTT) algorithm exploiting the clouds of points collected from a mmWave-FMCW radar is presented. The proposed solution consists of four main steps. First, the possible outliers of a raw radar data set are removed using a neural network model. Next, the cleaned-up radar data are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then, a Kalman Filter (KF) is used to track the position of the centroid of each cluster. Finally, a Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm is applied and updated over reasonably short time intervals to decide which detected tracks are supposed to be confirmed and which ones instead should be discarded. The proposed MTT technique was validated experimentally using the data sets collected from a 60-GHz TI IWR6843 radar platform. The reported results show that the developed algorithm, if properly tuned, is faster and returns more accurate results than other MTT techniques. In particular, the percentage of detection errors is negligible and the planar positioning accuracy is within about 30 cm with 90% probability when up to five targets move freely within the same room.

RCDAM-Net: A Foreign Object Detection Algorithm for Transmission Tower Lines Based on RevCol Network

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird’s nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms.

YOLO-T: Multi-Target Detection Algorithm for Transmission Lines

YOLO-T: Multi-Target Detection Algorithm for Transmission Lines

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Research on real-time perception method of key targets in the comprehensive excavation working face of coal mine

Aiming at the real-time perception problem of key target objects caused by harsh environmental factors of high dust, low illumination, motion blur, and multi-target mixing in the comprehensive excavation working face of coal mine, a multi-target detection and tracking algorithm based on DDEB-YOLOv5s and StrongSORT is proposed. First, the YOLOv5s model is improved by using C3-Dense module, decoupled head, ECIoU loss function, and weighted bi-directional feature pyramid network to enhance the detection performance of the model in complex backgrounds of coal mine and complete the design of the DDEB-YOLOv5s multi-target detection network. Second, the DDEB-YOLOv5s algorithm is used as a detector and combined with the StrongSORT tracking algorithm to track critical equipment and miners in the complex context of coal mine. Experiments were conducted on the dataset of comprehensive excavation working face, and the experimental results show that the proposed DDEB-YOLOv5s has the best integrated detection performance compared with other YOLO series target detection algorithms, and its mean value of detection accuracy reaches 91.7%, which is 4.9% higher than that of the original YOLOv5s model. In addition, compared to the three tracking models, (YOLOv7-tiny)-(BoT-SORT), YOLOv5s-DeepSORT, and YOLOv8s-Bytetrack, the (DDEB-YOLOv5s)-StrongSORT model has the best tracking performance (with a mean tracking accuracy of 94.2%) and the least number of identifier switches. Finally, the real-time perception method proposed in this study for the key target of the coal mine working face can provide new technical support and effective guarantee for coal mine safety production.

최대가능도 기반 저경사 FMICW 레이다 다중표적 탐지 알고리즘

최대가능도 기반 저경사 FMICW 레이다 다중표적 탐지 알고리즘

Real-time laser spot detection and tracking system based on parallel multi-target detection and determination algorithm.

Laser spot detection and tracking play a critical role in laser techniques. However, traditional detection and tracking systems tend to be bulky and lack portability. Therefore, there is a growing emphasis on developing high-performance and miniaturized systems based on the field programmable gate array (FPGA). In this paper, a novel parallel multi-target detection and determination algorithm is proposed to address the issue of current FPGA-based systems' ineffective detection of laser spots in complex environments. Our simulation results demonstrate that the algorithm can effectively detect laser spots in complex environments. It can process a frame with an 800 × 480 resolution in only 7.88ms at a 50MHz image processing frequency, which means it can process more than 100 f/s and meet the real-time detection requirements. Such excellent detection performance is challenging to achieve with central processing units and advanced RISC machine microprocessors. Then, the algorithm is further deployed on an FPGA to build a prototype laser spot detection and tracking system. Practical tests show that the system can achieve a spot detection accuracy of around 90% under different luminous intensities, indicating excellent robustness of the designed algorithm. Besides, with the use of a piezoelectric actuator, speedy and precise tracking of the laser spot is implemented. The characteristics of speedy response, self-latching in power off, and no electromagnetic interference of the piezoelectric actuator give the system tremendous advantages in developing high-precision wireless communication control technology, which further broadens the application of the proposed system.

Retracted: Taekwondo Trajectory Tracking Based on Multitarget Detection Algorithm

Retracted: Taekwondo Trajectory Tracking Based on Multitarget Detection Algorithm

Multitarget Detection Algorithm of UHV Line Fitting Infrared Image Based on YOLOv5

Multitarget Detection Algorithm of UHV Line Fitting Infrared Image Based on YOLOv5

A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

Reinforcement Learning Based Dual-Functional Massive MIMO Systems for Multi-Target Detection and Communications

Reinforcement learning (RL) based approaches in massive multiple input multiple output (mMIMO) arrays allow target detection in unknown environments. However, there are two main drawbacks hindering the practical application of these approaches: (i) poor detection performance for weak targets, and (ii) mismatch between high system overhead and single functionality. In light of this, we propose a dual-functional mMIMO (DF-mMIMO) system for multi-target detection with embedded communication in this work. First, we improve the RL based multi-target detection algorithm in both “action” and “reward” steps, by adding an omni-directional detection pulse in the action step and optimizing the reward mechanism in the reward step, so greatly improving the detection probability of weak targets in strong clutter. To achieve the dual modalities, communication information is embedded into the radar transmit waveform via complex beampattern modulation. In particular, we propose a low computational complexity two-step beamformer design method. First, the transmit waveform covariance matrix is designed via convex optimization, and then the beamforming weight matrix is determined according to closed-form formulas. Extensive simulation results demonstrate that the proposed DF-mMIMO system exhibits excellent target detection capability in a scenario where both strong and weak targets co-exist with downlink communications.

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks.

Dynamic Multitarget Detection Algorithm of Voxel Point Cloud Fusion Based on PointRCNN

Current 3D target detection methods used in the field of autonomous driving generally have low real-time performance and insufficient target context feature to detect dynamic multi-target accurately. In order to solve these problems, a dynamic multi-target detection algorithm of voxel point cloud fusion based on PointRCNN is proposed, which adopts a two-stage detection structure. The first stage directly processes the point cloud to extract key point features and divides voxel space. A novel submanifold sparse convolution is used to extract voxel features. Then key point features and voxel features of the point cloud are merged to generate pre-selection boxes. In the second stage, reference points are set based on the voxel features. The features of key points around reference points are merged for the second time to achieve optimized detection boxes. Finally, for the problem of inconsistent confidence, a mandatory consistency loss function is proposed to improve the accuracy of the detection box. The proposed algorithm was compared with other algorithms in three different datasets, and further tested on a self-made dataset from an actual vehicle platform. Results showed that the proposed algorithm had higher accuracy, better robustness, stronger generalization ability for dynamic multi-target detection.

Taekwondo Trajectory Tracking Based on Multitarget Detection Algorithm

In order to further improve the effect of fuzzy PID controller applied to joint robot trajectory tracking control, a Taekwondo trajectory tracking method based on multitarget detection algorithm is proposed in this study. Firstly, a fuzzy PID controller for trajectory tracking of joint robot is designed. Secondly, considering the two optimization objectives of controller output torque and trajectory tracking control deviation, an improved multiobjective PSO algorithm is designed to optimize the membership function and fuzzy rules of fuzzy PID controller. Finally, the multiobjective PSO algorithm and the improved multiobjective PSO algorithm are used to optimize the trajectory tracking fuzzy PID controller. The vector sets of the two optimization objectives are obtained, and the optimization results are compared and analyzed. The simulation results show that the simulation step length is t = 0.01 s, and the total simulation time is 10 s. Using the controller, the simulation values and output torque values of each joint angle at each time can be obtained, which are used to calculate the two objective function values in the optimization algorithm. According to the process, the optimization algorithm parameters are set, and the control simulation system designed above is run to complete the improved multiobjective PSO optimization of the trajectory tracking fuzzy PID controller of the joint robot. Conclusion. The improved multiobjective PSO algorithm has better nondominated solution set, which verifies the effectiveness and superiority of the algorithm to optimize the robot trajectory tracking fuzzy PID controller.

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition.

The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China's decorative ceramics industry.

A Dynamic Multitarget Detection Algorithm in front of Vehicle Based on Embedded System and Internet of Things

There are few studies for the classification detection and dynamic multitarget detection of the targets in front of vehicles. In order to solve this problem, a dynamic multitarget detection algorithm is proposed. First, a dynamic multitarget detection with displacement at any time is suggested; secondly, a multitarget detection algorithm based on improved You Only Look Once version 3 (YOLOv3) is proposed for the detection of multitarget high probability risk events in front of the vehicle. The YOLOv3 algorithm model is a lightweight backbone network that uses embedded real-time detection technologies. In this paper, we use a lightweight Mobilenetv2 to replace Darknet-53 for feature extraction. Moreover, an optimizer is used for multiobjective feature extraction, group normalization, and multiobjective feature extraction. The results show that in comparison with the original YOLOv3 algorithm, the detection leakage rate of the improved YOLOv3 multitarget detection algorithm is less than 5%, and the amount of model parameters in this paper is reduced by 95% as compared to the traditional data and CPU intertime is reduced to 78%.

A Multi-Target Tracking and Detection Algorithm for Wireless Sensor Networks

There are a large number of sensor nodes in wireless sensor network, whose main function is to process data scientifically, so that it can better sense and cooperate. In the network coverage, it can comprehensively collect the main information of the monitoring object, and send the monitoring data through short-range wireless communication to the gateway. Although there are many applications in WSNs, a multi-Target tracking and detection algorithm and the optimization problem of the wireless sensor networks are discussed in this paper. It can be obviously seen from the simulation results that this node cooperative program using particle CBMeMBer filtering algorithm can perfectly handle multi-target tracking, even if the sensor model is seriously nonlinear. Simulation results show that the tracking - forecasting data association scheme applying GM-CBMeMBer, which is proposed in this paper, runs well in identifying multiple target state, and can improve the estimation accuracy of multiple target state.

Research on the process of small sample non-ferrous metal recognition and separation based on deep learning

Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materials is difficult because of their different sources, various shapes and sizes, and complex surface conditions. In experimental study on the separation of these materials, few non-ferrous metal scraps can be used. To address these limitations, a traditional image recognition model and a small sample multi-target detection model (which can detect multiple targets simultaneously) based on deep learning and transfer learning were used to identify non-ferrous materials. The improved third version of You Only Look Once (YOLOv3) multi-target detection model using data augmentation, the loss function of focal loss, and a method of adjusting the threshold of Intersection over Union (IOU) between candidate bound and ground truth bound has superior target detection performance than methods. We obtained a 95.3% and 91.4% accuracy in identifying aluminum and copper scraps, respectively, and an operation speed of 18 FPS, meeting the real-time requirements of a sorting system. By using the improved YOLOv3 multi-target detection algorithm and equipment operation parameters selected, the accuracy and purity of the separation system exceeded 90%, meeting the needs of actual production.

Multi-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities

Wireless Sensor Network (WSNs) provides promising solutions for monitoring in several domains including industrial monitoring and control, home automation and smart cities, etc. There are numerous restrictions on the current development of target detection and tracking algorithms which makes them unsuitable or effective for indoor use. Such constraints include changes in the direction and changing target speeds, missing a target, and target dynamics. These issues come with difficulty in detection and tracking multiple targets. Moreover, the majority of the target tracking algorithms were presented on the conditions that the target is typically smooth with no unexpected changes that are difficult absolutely. Moreover, sensing coverage considers the crucial issue in a wireless sensor network. This paper implies an algorithm for detection and tracking of moving targets (intruders) for an indoor environment based on the probabilistic model utilizing WSN for safety and security. A mathematical model is presented to determine the optimum number of sensor nodes needed. The findings of the simulation showed that the MTDT algorithm provides a low missing target rate of less than 0.7 % for worst-case and can be utilized for different kinds of environment scenarios.