Target Recognition System Research Articles

Research on target detection and recognition system of autonomous driving based on deep learning

Abstract. Autonomous driving technology relies heavily on advanced target detection and recognition systems to ensure safety and efficiency. This essay explores how deep learning has revolutionized these systems in four key areas: lane detection, detection of obscured vehicle parts, blind spot detection, and multi-target detection. Traditional methods often struggle under challenging conditions, but deep learning approaches, including Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs), offer superior performance by analyzing complex features from raw data. Techniques like the Detection of Incomplete Vehicles using Deep Learning and Image Inpainting (DIDA) enhance safety by reconstructing obscured vehicle parts. In blind spot detection, models such as Sep-Res-SE blocks provide an effective, cost-efficient alternative to radar systems. The Adaptive Perceive SSD (AP-SSD) framework improves multi-target detection accuracy and real-time tracking by incorporating advanced feature extraction and temporal analysis. The essay concludes with future research directions aimed at refining real-time capabilities, expanding datasets, and exploring collaborative learning to further enhance autonomous driving technology.

Underwater Small Target Classification Using Sparse Multi-View Discriminant Analysis and the Invariant Scattering Transform

Sonar automatic target recognition (ATR) systems suffer from complex acoustic scattering, background clutter, and waveguide effects that are ever-present in the ocean. Traditional signal processing techniques often struggle to distinguish targets when noise and complicated target geometries are introduced. Recent advancements in machine learning and wavelet theory offer promising directions for extracting informative features from sonar return data. This work introduces a feature extraction and dimensionality reduction technique using the invariant scattering transform and Sparse Multi-view Discriminant Analysis for identifying highly informative features in the PONDEX09/PONDEX10 datasets. The extracted features are used to train a support vector machine classifier that achieves an average classification accuracy of 97.3% using six unique targets.

Retraction Note: Simulation of image feature extraction based on optical sensors in basketball target recognition system

Retraction Note: Simulation of image feature extraction based on optical sensors in basketball target recognition system

UAWC: An intelligent underwater acoustic target recognition system for working conditions mismatching

Underwater acoustic target recognition (UATR) systems are crucial to both military and civilian activities. However, the complex ship working conditions will largely affect the performance of recognition systems, especially in the case of working conditions mismatching (WCMM). For WCMM problems, an intelligent UATR system for working condition mismatching (UAWC) is proposed. UAWC uses auditory features as input to the system and uses knowledge distillation to learn the intrinsic connections of target features under different working conditions. In the proposed approach, the teacher network obtains initial knowledge by utilizing a large amount of existing working condition data. Next, the student network uses a small amount of target working data for training, and extracts incremental knowledge in teacher network through knowledge distillation technology to enhance the accuracy of its classification of target working data, so as to effectively deal with the WCMM problem.The tests make use of datasets for ship-radiated noise under various working conditions.The results showed that UAWC performs better than other methods on a wide range of WCMM problems.

PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task.

Accurate classification of single-trial electroencephalogram (EEG) is crucial for EEG-based target image recognition in rapid serial visual presentation (RSVP) tasks. P300 is an important component of a single-trial EEG for RSVP tasks. However, single-trial EEG are usually characterized by low signal-to-noise ratio and limited sample sizes. Given these challenges, it is necessary to optimize existing convolutional neural networks (CNNs) to improve the performance of P300 classification. The proposed CNN model called PSAEEGNet, integrates standard convolutional layers, pyramid squeeze attention (PSA) modules, and deep convolutional layers. This approach arises the extraction of temporal and spatial features of the P300 to a finer granularity level. Compared with several existing single-trial EEG classification methods for RSVP tasks, the proposed model shows significantly improved performance. The mean true positive rate for PSAEEGNet is 0.7949, and the mean area under the receiver operating characteristic curve (AUC) is 0.9341 (p < 0.05). These results suggest that the proposed model effectively extracts features from both temporal and spatial dimensions of P300, leading to a more accurate classification of single-trial EEG during RSVP tasks. Therefore, this model has the potential to significantly enhance the performance of target recognition systems based on EEG, contributing to the advancement and practical implementation of target recognition in this field.

Single-metalens-assisted polarization imaging and edge detection for target recognition

Simultaneous capture of various light information, including polarization and edge information of the objects, has consistently been a fundamental concern within the field of target recognition. However, these tasks are typically accompanied by bulky optical components and active illumination methods, which significantly restricts their use in compact and lightweight applications. Here, we demonstrate a metalens-assisted imaging system that can simultaneously achieve polarization imaging and optoelectronic edge detection in a single shot with low consumption. The dielectric metalens is designed to achieve polarization imaging by dispersing the input polarized light into two orthogonal components, resulting in optoelectronic isotropic edge detection of two-dimensional images after digital post-processing. Compared with the algorithmic methods using a convolution kernel, the proposed system has a much lower computational complexity. The work presented in this study demonstrates the potential applications in machine vision and paves the way for the development of compact target recognition and real-time image processing systems.

Multiband task related components enhance rapid cognition decoding for both small and similar objects

The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.

Simulation of image feature extraction based on optical sensors in basketball target recognition system

Simulation of image feature extraction based on optical sensors in basketball target recognition system

CVPCNN: Conditionally variational parameterized convolution neural network for HRRP target recognition with imperfect side information

Radar target recognition tasks often incorporate valuable side information (SI) that aids in the recognition task. The effectiveness of SI has been fully verified in traditional statistical modeling approaches. Its full potential remains untapped in deep learning-based high-resolution range profile (HRRP) target recognition tasks. Taking into account the azimuth sensitivity of HRRP, azimuth is chosen as the SI of the proposed model. To integrate SI into deep neural networks, we propose two novel parameterized convolution methods, namely variational parameterized convolution (VPCONV) and the extended conditionally VPCONV (CVPCONV). Specifically, VPCONV makes the convolutional kernel weights as the function of the auxiliary azimuth, which gives the network the capability of adaptively adjusting to changes in azimuth. Acknowledging the challenge of estimating HRRP azimuth accurately in practical scenarios, VPCONV employs a heteroscedastic Gaussian distribution, featuring varying mean and variance, to generalize the estimation error of SI through variational encoding. Moreover, CVPCONV incorporates the properties of the HRRP samples themselves by kernel attention module. Finally, we present a lightweight SI-based network. The experimental results based on the measured HRRPs validate the effectiveness of proposed method across various extended recognition tasks, underscoring the potential of fusing SI via parameterized convolution in advancing target recognition systems.

Strong smog penetrating capability of quantum cascade laser at 8.9 micrometre

To solve the problem that existing near-infrared (NIR) target recognition system cannot penetrate the dense smog, in long-wave infrared (LWIR) target recognition system the smog penetrating capability of quantum cascade laser (QCL) at 8.9 micrometre has been studied. A normalized multiple-particle distribution model has been built firstly and increase threshold values have been derived. According to the calculation, the LWIR scattering and extinction parameters are lower than NIR corresponding parameters. Moreover, the echo power of LWIR detection laser is 2–3 orders of magnitude higher than that of NIR detection laser in dense smog, and the echo signal-to-noise ratio (SNR) is higher 40 dB–80 dB than that of NIR laser. Finally, The strong penetration capability of the 8.9μm QCL is also demonstrated experimentally by comparing it with 0.65μm laser, the maximum value of the its penetration distance is up to 36 m by using 30 mirrors with the total optical path of 71.3 m, and its attenuation parameter is less than <1%/m in the dense smog. Based on the presented results, the application of LWIR QCL in dense smog will be broadened.

Electrochemiluminescence of hot exciton nanomaterial with boosted efficiency for visual bioanalysis

The electrochemiluminescence (ECL) efficiency (ΦECL) of organic ECL emitters is greatly influenced by the excited state property and component. Here we design a hot exciton molecule (BCzP-BT) with hybridized local and charge-transfer (HLCT) excited state property to prepare the first hot exciton organic nanorods (BB NRs). The BB NRs exhibit both sensitive annihilation and intense coreactant ECL emissions with a band gap emission model. Due to the reverse intersystem crossing among high-lying excited states (hRISC), the HLCT excited state property leads to highly efficient utilization of triplet excitons and thus high photoluminescence quantum yields (ΦPL) of more than 80% at 554 nm, which is demonstrated by solvatochromic experiments and quantum chemical calculations. The high ΦPL endows BB NRs with superior ΦECL over other nanoemitters, even the thermally activated delayed fluorescence materials, and thus offers highly-efficient nanoemitters for the design of ECL imaging strategy. As a proof of concept, an ECL probe is constructed by assembling BHQ2-ssDNA on BB NRs to integrate CRISPR/Cas12a system for target recognition, which produces a rapid and high-throughput ECL imaging platform. The proposed imaging method for HPV16 DNA detection shows excellent performance with a detection limit of 0.6 fM. This work broadens the application of hot exciton materials in ECL field, and opens a new avenue for developing next-generation ECL devices.

FPC-BTB detection and positioning system based on optimized YOLOv5

With the aim of addressing the visual positioning problem of board-to-board (BTB) jacks during the automatic assembly of flexible printed circuit (FPC) in mobile phones, an FPC-BTB jack detection method based on the optimized You Only Look Once, version 5 (YOLOv5) deep learning algorithm was proposed in this study. An FPC-BTB jack real-time detection and positioning system was developed for the real-time target detection and pose output synchronization of the BTB jack. On that basis, a visual positioning experimental platform that integrated a UR5e manipulator arm and Hikvision industrial camera was built for BTB jack detection and positioning experiments. As indicated by the experimental results, the developed FPC-BTB jack detection and positioning system for BTB target recognition and positioning achieved a success rate of 99.677%. Its average detection accuracy reached 99.341%, the average confidence of the detected target was 91%, the detection and positioning speed reached 31.25 frames per second, and the positioning deviation was less than 0.93 mm, which conforms to the practical application requirements of the FPC assembly process.

Target recognition and detection system based on sensor and nonlinear machine vision fusion

Abstract In order to realize the automatic detection system of electric sensor, a method based on sensor and nonlinear machine vision is proposed. Aiming at complex scenes and dynamic changes in target recognition and detection in large-scale industrial field, a target recognition and detection system based on the fusion of vision sensor and nonlinear machine vision is proposed. The system introduces nonlinear features and uses deep neural network to realize multi-scale analysis and recognition of image data on the basis of traditional machine vision. The system uses C++ language development and has a good user interface. The photoelectric sensor weld image is collected by machine vision technology, the target area of the image is detected by Gaussian model, the feature points of the target area are extracted by combining Hessian matrix, the extracted feature points are input into the quantum gate neural network model, and the recognition results are obtained. The simulation results show that the author’s method has the highest value among the three test indicators, with the highest accuracy rate of 97%, the highest recall rate of 98%, and the highest F1 value of 94. The time consumed by the author’s method for automatic identification of photoelectric sensor welding is within 6 s, the time spent on the film wall recognition method for automatic identification of photoelectric sensor welding is within 20 s, and the time spent by the feature extraction and identification method for automatic identification of photoelectric sensor weld is within 22 s. It has been proven that the method based on the fusion of sensors and nonlinear machine vision can achieve an automatic recognition and detection system for electrical sensor welds. The object detection and recognition method proposed in this article can be applied to dynamic changes and complex scenes in various complex backgrounds and has a good application prospect. The system proposed in this article has some limitations, such as the algorithm in the calculation accuracy, real-time, and other aspects that have room for improvement.

A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head

Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions.

Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array

Spiking neural networks inspired by biological models are gaining popularity in artificial intelligence due to their ability to solve diverse problems while reducing energy consumption. As a result of the trade-off between the need to transmit large amounts of data and the power consumption of hardware deployment, artificial vision systems are particularly well-suited to construction using spiking neural networks (SNNs). How to communicate with the neuromorphic network effectively is one of the challenges associated with building systems that utilize SNN systems. It is necessary to convert the data to spike form before they can be processed by an SNN as input, unless neuromorphic or event-triggered sensing systems are employed. We present a configurable circuit based on a focal plane array (FPA) capable of providing spike-encoded readout data at the pixel level. With this type of circuit, the current signal of the photoelectric sensor can be encoded into two spike encodings with different precision, which are sent for processing to SNNs. This provides image information at two different scales for the artificial vision system based on SNNs. With this feature, we can use this circuit and different SNN structures to build an artificial target recognition system that is closer to the biological visual system.

Underwater Acoustic Target Recognition Based on Deep Residual Attention Convolutional Neural Network

Underwater acoustic target recognition methods based on time-frequency analysis have shortcomings, such as missing information on target characteristics and having a large computation volume, which leads to difficulties in improving the accuracy and immediacy of the target recognition system. In this paper, an underwater acoustic target recognition model based on a deep residual attention convolutional neural network called DRACNN is proposed, whose input is the time-domain signal of the underwater acoustic targets radiated noise. In this model, convolutional blocks with attention to the mechanisms are used to focus on and extract deep features of the target, and residual networks are used to improve the stability of the network training. On the full ShipsEar dataset, the recognition accuracy of the DRACNN model is 97.1%, which is 2.2% higher than the resnet-18 model with an approximately equal number of parameters as this model. With similar recognition accuracies, the DRACNN model parameters are 1/36th and 1/10th of the AResNet model and UTAR-Transformer model, respectively, and the floating-point operations are 1/292nd and 1/46th of the two models, respectively. Finally, the DRACNN model pre-trained on the ShipsEar dataset was migrated to the DeepShip dataset and achieved recognition accuracy of 89.2%. The experimental results illustrate that the DRACNN model has excellent generalization ability and is suitable for a micro-UATR system.

Research on Vision of Intelligent Car Based on Broad Learning System.

The broad learning system (BLS) of intelligent vehicle in different target environments is studied in this article. First, this article provides with the target recognition image data to be trained and detected through the automated guided vehicle (AGV) mobile platform, which can grab the recognition image of different angles and backgrounds. In order to avoid the data generalization phenomenon, the dataset can be expanded by the data normalization and data enhancement. Second, the data are input into the shared convolution layer to extract the feature image and maintain the image. The parameters of image height, width, and channel number are invariable, and the new feature image is obtained by further extraction. Furthermore, the region proposal network (RPN) prefiltering algorithm based on hierarchical clustering is used to filter the objects in the candidate box to determine the region image corresponding to the feature image. Then, the feature images of different sizes input into region of interest (ROI) pooling are used to keep the size of the image in the ROI consistent. Finally, the normalized image is input into the classifier module to obtain the category of the target recognition image to be detected. Through the simulation experiments of different groups, it can be seen that the target recognition system proposed in this design can not only accurately detect the objects but also stably recognize the objects in different environments. The target recognition accuracy for the optimized system is about 95%.

Maritime Target Recognition and Location System Based on Lightweight Neural Network

China’s sea surface area is vast, the need to monitor the area is too large, and the traditional human monitoring method consumes a lot of manpower. Additionally, the monitoring period is too long; the monitoring efficiency is too low; and long-term human monitoring can easily cause visual fatigue, as well as missed detection and error detection. At present, the detection of sea surface targets generally includes infrared, visible light and other different means, which can obtain the image information of sea surface targets in different ways. The infrared target detection of the sea surface can be processed in the spatial domain and frequency domain, respectively, but the image resolution is not high in general, and the detection effect is not good because it is easily affected by weather. In this paper, we propose a maritime target detection method based on embedded vision. Based on visible video images, this paper realizes the rapid detection and recognition of sea surface targets. Clouds and waves in ocean images are filtered by adding an image preprocessing module. Compared with the traditional two-frame difference method, this algorithm has better detection capability for sea surface targets. Experiments were carried out in different weather conditions to detect moving ships at sea. By comparing the number of detection boxes and the detection accuracy, the accuracy of this method reaches 90.2 percent. By designing a single camera location algorithm for the marine environment, the world coordinate location of the marine target is realized. On this basis, the communication function is added to realize the intelligent monitoring of the sea surface without human intervention.

Target recognition and tracking system based on UAV platform

Machine learning can extract the features of the target from the known image dataset and detect the target quickly by using the trained deep neural network. Due to UAVs’ fast flight speed and long detection distance, real-time and accurate target detection requirements are relatively high. For such scenarios,a ground object recognition and tracking method that integrates the YOLOv5 target detection algorithm and KCF target tracking algorithm are proposed. The combination of the two algorithms can meet the requirements of recognition speed, recognition rate, and tracking accuracy in the current scene. Two algorithms are run simultaneously in the recognition process, the detection frame with the highest confidence is selected as the final detection result, and the detected target is tracked by the KCF target tracking algorithm based on correlation filtering. The experimental results show that this method not only meets the requirements of recognition distance, recognition accuracy, and recognition speed of ground target but also can track a ground target in real-time, with strong robustness and real-time performance.

A Study on Energy Efficiency of a Distributed Processing Scheme for Image-Based Target Recognition for Internet of Multimedia Things

A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. In depth, image-based sensing and transmission in IoMT significantly drain the sensor energy and overwhelm the network with redundant data. Event-based sensing schemes can be used to provide efficient data transmission and an extended network lifetime. This paper proposes a novel approach for distributed event-based sensing achieved by a cluster of processing nodes. The proposed scheme aims to balance the processing load across the nodes in the cluster. This study demonstrates the adequacy of distributed processing to extend the lifetime of the IoMT platform and compares the efficiency of Haar wavelet decomposition and general Fourier descriptors (GFDs) as a feature extraction module in a distributed features-based target recognition system. The results show that the distributed processing of the scheme based on the Haar wavelet transform of the image outperforms the scheme based on a general Fourier shape descriptor in recognition accuracy of the target as well as the energy consumption. In contrast to a GFD-based scheme, the recognition accuracy of a Haar-based scheme was increased by 26%, and the number of sensing cycles was increased from 40 to 70 cycles, which attests to the adequacy of the proposed distributed Haar-based processing scheme for deployment in IoMT devices.