Target Recognition Algorithm Research Articles

Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery

Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or unknown objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes. Specifically, we do not alter the training strategy of the main classifier so that its performance on known classes remains unchanged. Instead, we introduce a second network (trained using regression) that uses the decision of the primary classifier to produce a class conditional score that indicates whether an input object is indeed a known object. This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. Most importantly, our method does not require any examples of OOD imagery to be used for training the second network. For illustrative purposes, we demonstrate the effectiveness of the proposed method using the CIFAR-10 dataset. Ultimately, our goal is to classify known targets in infra-red images while improving the ability to reject unknown classes. Towards this end, we train and test our method on a public domain medium-wave infra-red (MWIR) dataset provided by the US Army for the development of automatic target recognition (ATR) algorithms. The results of this experiment show that the proposed method outperforms other state-of-the-art methods in rejecting the unknown target types while accurately classifying the known ones.

Improved YOLOv8n based helmet wearing inspection method

This paper proposes the YOLOv8n_H method to address issues regarding parameter redundancy, slow inference speed, and suboptimal detection precision in contemporary helmet-wearing target recognition algorithms. The YOLOv8 C2f module is enhanced with a new SC_Bottleneck structure, incorporating the SCConv module, now termed SC_C2f, to mitigate model complexity and computational costs. Additionally, the original Detect structure is substituted with the PC-Head decoupling head, leading to a significant reduction in parameter count and an enhancement in model efficiency. Moreover, the original Detect structure is replaced by the PC-Head decoupling head, significantly reducing parameter count and enhancing model efficiency. Finally, regression accuracy and convergence speed are boosted by the dynamic non-monotonic focusing mechanism introduced through the WIoU boundary loss function. Experimental results on the expanded SHWD dataset demonstrate a 46.63% reduction in model volume, a 44.19% decrease in parameter count, a 54.88% reduction in computational load, and an improvement in mean Average Precision (mAP) to 93.8% compared to the original YOLOv8 algorithm. In comparison to other algorithms, the model proposed in this paper markedly reduces model size, parameter count, and computational load while ensuring superior detection accuracy.

A positioning method for AUV based on guiding light source

This paper proposes a method for determining the position of underwater vehicles using light vision guidance. In order to solve the problem of limited perception distance and unclear underwater images, the illumination device is installed on the recovery platform as a light source to enhance the feature information of the recovery platform. The collected underwater light source images are enhanced by the dark channel prior defogging algorithm, and then the YOLOv5 algorithm is used to recognize the targets. Through experiments, it is verified that the image enhancement algorithm and target recognition algorithm are effective. Using the recognition results, the positioning accuracy of the binocular camera ranging algorithm is verified, and the results indicate that the proposed method can accurately determine the distance of the AUV relative to the recovery platform.

Research progress and prospect of key technologies of fruit target recognition for robotic fruit picking.

It is crucial for robotic picking fruit to recognize fruit accurately in orchards, this paper reviews the applications and research results of target recognition in orchard fruit picking by using machine vision and emphasizes two methods of fruit recognition: the traditional digital image processing method and the target recognition method based on deep learning. Here, we outline the research achievements and progress of traditional digital image processing methods by the researchers aiming at different disturbance factors in orchards and summarize the shortcomings of traditional digital image processing methods. Then, we focus on the relevant contents of fruit target recognition methods based on deep learning, including the target recognition process, the preparation and classification of the dataset, and the research results of target recognition algorithms in classification, detection, segmentation, and compression acceleration of target recognition network models. Additionally, we summarize the shortcomings of current orchard fruit target recognition tasks from the perspectives of datasets, model applicability, universality of application scenarios, difficulty of recognition tasks, and stability of various algorithms, and look forward to the future development of orchard fruit target recognition.

Research on Pulse Linear Array LiDAR Point Cloud Enhancement Algorithm in Snowy Environment

Abstract LiDAR point cloud data acquisition is difficult in snowfall environment, which leads to fewer point cloud datasets in snowy days and affects the subsequent research on target recognition algorithms in bad weather. In this paper, we investigate a point cloud enhancement algorithm based on pulse linear array LiDAR in snowfall environment. We use a linear array data acquisition method to collect the point cloud data of sunny and snowy days, and the point cloud data of sunny days and different degrees of snowy days are compared. We establish a linear array pulse LiDAR acquisition model and a single-line laser snowfall simulation model. The results show that a large number of snow particles gather in the vicinity of pulse linear array LiDAR and they are conical in distribution. The simulation model can generate point cloud data close to the real snow environment.

Pomelo fruit-picking robot for unstructured environments

Abstract The adoption of mechanized and intelligent harvesting equipment is an effective approach to enhancing the stability and competitiveness of the pomelo industry. This study presents the development of a harvesting device tailored for pomelos by improving target recognition algorithms and optimizing the picking mechanism. A fruit stem posture fitting algorithm based on the morphological characteristics of pomelos was developed, capable of obtaining spatial information on fruit stems even in obstructed environments. A serial PPRP picking mechanism with a fault-tolerant end-effector was designed, and kinematic equations and control theory models were established. Experimental results demonstrate that the workspace of the designed robotic arm for pomelo harvesting has a height of 1.9 meters and a depth of 1.3 meters, with a recognition accuracy of 94% and a picking success rate of 72%. The harvesting efficiency reached two fruits per minute, making the device suitable for operations in both structured and unstructured orchard environments.

Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm.

Underwater object detection is highly complex and requires a high speed and accuracy. In this paper, an underwater target detection model based on YOLOv8 (SPSM-YOLOv8) is proposed. It solves the problems of high computational complexities, slow detection speeds and low accuracies. Firstly, the SPDConv module is utilized in the backbone network to replace the standard convolutional module for feature extraction. This enhances computational efficiency and reduces redundant computations. Secondly, the PSA (Polarized Self-Attention) mechanism is added to filter and enhance the polarization of features in the channel and spatial dimensions to improve the accuracy of pixel-level prediction. The SCDown (spatial-channel decoupled downsampling) downsampling mechanism is then introduced to reduce the computational cost by decoupling the space and channel operations while retaining the information in the downsampling process. Finally, MPDIoU (Minimum Point Distance-based IoU) is used to replace the CIoU (Complete-IOU) loss function to accelerate the convergence speed of the bounding box and improve the bounding box regression accuracy. The experimental results show that compared with the YOLOv8n baseline model, the SPSM-YOLOv8 (SPDConv-PSA-SCDown-MPDIoU-YOLOv8) detection accuracy reaches 87.3% on the ROUD dataset and 76.4% on the UPRC2020 dataset, and the number of parameters and amount of computation decrease by 4.3% and 4.9%, respectively. The detection frame rate reaches 189 frames per second on the ROUD dataset, thus meeting the high accuracy requirements for underwater object detection algorithms and facilitating lightweight and fast edge deployment.

Automatic Reading and Reporting Weather Information from Surface Fax Charts for Ships Sailing in Actual Northern Pacific and Atlantic Oceans

This study is aimed to improve the intelligence level, efficiency, and accuracy of ship safety and security systems by contributing to the development of marine weather forecasting. The accurate and prompt recognition of weather fax charts is very important for navigation safety. This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. This enabled the expansion of an existing auto-response marine forecasting system’s applications toward north Pacific and Atlantic Oceans, thus enhancing decision-making capabilities and response measures for sailing ships at actual sea. The OpenCV image processing method and YOLOv5s/YOLO8vn algorithm were utilized to make template matches and locate warning symbols and weather reports from surface weather charts. After these improvements, the average accuracy of the model significantly increased from 0.920 to 0.928, and the detection rate of a single image reached a maximum of 1.2 ms. Additionally, OCR technology was applied to retract texts from weather reports and highlighted the marine areas where dense fog and great wind conditions are likely to occur. Finally, the field tests confirmed that this auto and intelligent system could assist the navigator within 2–3 min and thus greatly enhance the navigation safety in specific areas in the sailing routes with minor text-based communication costs.

A Ship’s Maritime Critical Target Identification Method Based on Lightweight and Triple Attention Mechanisms

The ability to classify and recognize maritime targets based on visual images plays an important role in advancing ship intelligence and digitalization. The current target recognition algorithms for common maritime targets, such as buoys, reefs, other ships, and bridges of different colors, face challenges such as incomplete classification, low recognition accuracy, and a large number of model parameters. To address these issues, this paper proposes a novel maritime target recognition method called DTI-YOL (DualConv Triple Attention InnerEIOU-You Only Look Once). This method is based on a triple attention mechanism designed to enhance the model’s ability to classify and recognize buoys of different colors in the channel while also making the feature extraction network more lightweight. First, the lightweight double convolution kernel feature extraction layer is constructed using group convolution technology to replace the Conv structure of YOLOv9 (You Only Look Once Version 9), effectively reducing the number of parameters in the original model. Second, an improved three-branch structure is designed to capture cross-dimensional interactions of input image features. This structure forms a triple attention mechanism that accounts for the mutual dependencies between input channels and spatial positions, allowing for the calculation of attention weights for targets such as bridges, buoys, and other ships. Finally, InnerEIoU is used to replace CIoU to improve the loss function, thereby optimizing loss regression for targets with large scale differences. To verify the effectiveness of these algorithmic improvements, the DTI-YOLO algorithm was tested on a self-made dataset of 2300 ship navigation images. The experimental results show that the average accuracy of this method in identifying seven types of targets—including buoys, bridges, islands and reefs, container ships, bulk carriers, passenger ships, and other ships—reached 92.1%, with a 12% reduction in the number of parameters. This enhancement improves the model’s ability to recognize and distinguish different targets and buoy colors.

A lightweight defect detection algorithm for escalator steps

In this paper, we propose an efficient target detection algorithm, ASF-Sim-YOLO, to address issues encountered in escalator step defect detection, such as an excessive number of parameters in the detection network model, poor adaptability, and difficulties in real-time processing of video streams. Firstly, to address the characteristics of escalator step defects, we designed the ASF-Sim-P2 structure to improve the detection accuracy of small targets, such as step defects. Additionally, we incorporated the SimAM (Similarity-based Attention Mechanism) by combining SimAM with SPPF (Spatial Pyramid Pooling-Fast) to enhance the model’s ability to capture key information by assigning importance weights to each pixel. Furthermore, to address the challenge posed by the small size of step defects, we replaced the traditional CIoU (Complete-Intersection-over-Union) loss function with NWD (Normalized Wasserstein Distance), which alleviated the problem of defect missing. Finally, to meet the deployment requirements of mobile devices, we performed channel pruning on the model. The experimental results showed that the improved ASF-Sim-YOLO model achieved an average accuracy (mAP50) of 96.8% on the test data set, which was a 22.1% improvement in accuracy compared to the baseline model. Meanwhile, the computational complexity (in GFLOPS) of the model was reduced to a quarter of that of the baseline model, while the frame rate (FPS) was improved to 575.1. Compared with YOLOv3-tiny, YOLOv5s, YOLOv8s, Faster-RCNN, TOOD, RTMDET and other deep learning-based target recognition algorithms, ASF-Sim-YOLO has better detection accuracy and real-time processing capability. These results demonstrate that ASF-Sim-YOLO effectively balances lightweight design and performance improvement, making it highly suitable for real-time detection of step defects, which can meet the demands of escalator inspection operations.

Research on the Identification and Classification of Marine Debris Based on Improved YOLOv8

Autonomous underwater vehicles equipped with target recognition algorithms are a primary means of removing marine debris. However, due to poor underwater visibility, light scattering by suspended particles, and the coexistence of organisms and debris, current methods have problems such as poor recognition and classification effects, slow recognition speed, and weak generalization ability. In response to these problems, this article proposes a marine debris identification and classification algorithm based on improved YOLOv8. The algorithm incorporates the CloFormer module, a context-aware local enhancement mechanism, into the backbone network, fully utilizing shared and context-aware weights. Consequently, it enhances high- and low-frequency feature extraction from underwater debris images. The proposed C2f-spatial and channel reconstruction (C2f-SCConv) module combines the SCConv module with the neck C2f module to reduce spatial and channel redundancy in standard convolutions and enhance feature representation. WIoU v3 is employed as the bounding box regression loss function, effectively managing low- and high-quality samples to improve overall model performance. The experimental results on the TrashCan-Instance dataset indicate that compared to the classical YOLOv8, the mAP@0.5 and F1 scores are increased by 5.7% and 6%, respectively. Meanwhile, on the TrashCan-Material dataset, the mAP@0.5 and F1 scores also improve, by 5.5% and 5%, respectively. Additionally, the model size has been reduced by 12.9%. These research results are conducive to maintaining marine life safety and ecosystem stability.

Research on Improved Lightweight Fish Detection Algorithm Based on Yolov8n

The fish detection algorithm is of great significance for obtaining aquaculture information, optimizing prey allocation in aquaculture, and improving the growth efficiency and survival rate of fish. To address the challenges of high complexity, large computational load, and limited equipment resources in existing fish target detection processes, a lightweight fish detection and recognition method based on the Yolov8 network, called the CUIB-YOLO algorithm, is proposed. This method introduces a secondary innovative C2f-UIB module to replace the original C2f module in the Yolov8 neck network, effectively reducing the model’s parameter count. Additionally, the EMA mechanism is incorporated into the neck network to enhance the feature fusion process. Through optimized design, the Params and FLOPs of the CUIB-YOLO algorithm model are reduced to 2.5 M and 7.5 G, respectively, which represent reductions of 15.7% and 7.5% compared to the original YOLOv8n model. The mAP @ 0.5–0.95/% value reaches 76.4%, which is nearly identical to that of the Yolov8n model. Experimental results demonstrate that compared with current mainstream target detection and recognition algorithms, the proposed model reduces computational load without compromising detection accuracy, achieves model lightweighting, improves inference speed, and enhances the algorithm’s real-time performance.

Research on Visual Recognition of Service Robots Based on YOLO Algorithm

In modern society, based on the aging of population and the rapid development of intelligent technology, the demand for intelligent service robots is increasing, and visual recognition is one of the key technologies of service robots. The rapid development of deep learning technology provides new solutions for visual recognition. Existing target recognition algorithms have the problem of complex model and slow inference speed. According to the above problems, this paper proposes a visual recognition algorithm based on YOLO series, which is used for the visual recognition design scheme of service robot. It can ensure the detection accuracy, reduce the speed of the model complexity, speed up the reasoning, and determine whether to meet the actual needs. Through randomly selected pictures, the results show that the design scheme can effectively improve the detection accuracy and real-time of the service robot on targets, and enhance its autonomous service ability in various complex environments.

Key Technologies for Autonomous Fruit- and Vegetable-Picking Robots: A Review

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management.

Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks.

Coded target recognition algorithm for vision measurement

Coded target recognition algorithm for vision measurement

Infrared Image Enhancement Method of Substation Equipment Based on Self-Attention Cycle Generative Adversarial Network (SA-CycleGAN)

During the acquisition of infrared images in substations, low-quality images with poor contrast, blurred details, and missing texture information frequently appear, which adversely affects subsequent advanced visual tasks. To address this issue, this paper proposes an infrared image enhancement algorithm for substation equipment based on a self-attention cycle generative adversarial network (SA-CycleGAN). The proposed algorithm incorporates a self-attention mechanism into the CycleGAN model’s transcoding network to improve the mapping ability of infrared image information, enhance image contrast, and reducing the number of model parameters. The addition of an efficient local attention mechanism (EAL) and a feature pyramid structure within the encoding network enhances the generator’s ability to extract features and texture information from small targets in infrared substation equipment images, effectively improving image details. In the discriminator part, the model’s performance is further enhanced by constructing a two-channel feature network. To accelerate the model’s convergence, the loss function of the original CycleGAN is optimized. Compared to several mainstream image enhancement algorithms, the proposed algorithm improves the quality of low-quality infrared images by an average of 10.91% in color degree, 18.89% in saturation, and 29.82% in feature similarity indices. Additionally, the number of parameters in the proposed algorithm is reduced by 37.89% compared to the original model. Finally, the effectiveness of the proposed method in improving recognition accuracy is validated by the Centernet target recognition algorithm.

Attribute Feature Perturbation-Based Augmentation of SAR Target Data.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

Deep-Autoencoder-Based Radar Source Recognition: Addressing Large-Scale Imbalanced Data and Edge Computing Constraints

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition.

Line spectrum target recognition algorithm based on time‐delay autoencoder

AbstractEffective extraction of target features has always been a key issue in target recognition technology in the field of signal processing. Traditional deep learning algorithms often require extensive data for pre‐training models to ensure the accuracy of feature extraction. Moreover, it is challenging to completely remove noise due to the complexity of the underwater environment. A Time‐Delay Autoencoder (TDAE) is employed to extract ship‐radiated noise characteristics by leveraging the strong coherent properties of line spectrum. This approach eliminates the need for previous data to adaptively develop a nonlinear model for line spectrum extraction. The test data was processed using three distinct approaches, and plots of recognition accuracy curves at various signal‐to‐noise ratios were made. On the dataset utilised in the research, experimental results show that the proposed approach achieves over 75% recognition accuracy, even at a signal‐to‐noise ratio of −15 dB.