Network For Recognition Research Articles

Feature-semantic augmentation network for few-shot open-set recognition

Few-shot open-set recognition (FSOR) represents a relatively underexplored area of research. The primary challenge encountered by FSOR methods lies in recognizing known classes while simultaneously rejecting unknown classes utilizing only limited samples. Current FSOR methods predominantly rely on the visual information extracted from images to establish class representations, aiming to derive distinguishable classification scores for both known and unknown classes. However, these methods often overlook the benefits of leveraging semantic information derived from class names associated with images, which could provide valuable auxiliary learning insights. This study introduces a feature-semantic augmentation network to improve FSOR performance utilizing multimodal information. Specifically, we augment the class-specific features of closed-set prototypes by integrating visual and textual features from known class names across both local and global feature spaces. To facilitate prototype learning, We introduce a refinement and fusion module. Among these, the former leverages the similarity between prototype and target features at both channel and spatial dimensions to calibrate targets relative to their relevant prototypes. Meanwhile, the latter employs additional classification targets generated by the fusion module to provide learning sources from different classes. Experimental results on various few-shot learning benchmarks show that the proposed method significantly outperforms current state-of-the-art methods across both closed- and open-set scenarios.

Generative Adversarial Networks for SAR Automatic Target Recognition and Classification Models Enhanced Explainability: Perspectives and Challenges

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers.

Adaptive shift graph convolutional neural network for hand gesture recognition based on 3D skeletal similarity

Adaptive shift graph convolutional neural network for hand gesture recognition based on 3D skeletal similarity

Deep learning approach for automatic speech recognition in the presence of noise

Accurate recognition of speech in noisy environment is still an obstacle for wider application of speech recognition technology. The robustness of a speech recognition system is heavily influenced by the ability to handle the presence of background noise. In this research work, we propose a model based on Deep Fourier Neural Network (DFNN) for Automatic Speech Recognition (ASR) using LibriSpeech dataset. Most of the existing speech recognition techniques lack the robustness of handling background noise, as a result these techniques are not applicable in real-time. In order to mitigate the challenges of background noise, this research work proposes an efficient recognition technique which analyses in detail the raw audio waveforms using the Deep Fourier Neural Network (DFNN). This novel deep learning approach has a concise architecture and is an efficient approach for automatic speech recognition. The proposed deep learning approach embeds the Fourier transform, which is one of the most popular feature representations transform for audio signal processing. The Fourier transform extracts the core information from waveforms in the form of short term spectra of the speech signal as a function of time. The extracted short term spectra are analyzed deeply in the proposed DFNN model for accurate speech recognition in the presence of noise.

RNE-DSNet: A Re-parameterization Neighborhood Enhancement-based Dual-Stream Network for CT image recognition

Deep learning models based on Transformer and CNN are current research hotspots. However, there are more time complexity and space complexity in extracting global–local features, the feature extraction ability is insufficient for small lesions in CT images. A Re-parameterization Neighborhood Enhancement-based Dual-Stream Network (RNE-DSNet) is proposed for CT image recognition in this paper. This method improves the ability to recognize small lesions while reducing the Params number and FLOPs, which gives a trade-off between performance and cost. The main works are as following: Firstly, a Re-parameterization Neighborhood Enhancement-based Dual-Stream Network is designed to improve the global–local feature extraction ability. Secondly, the Re-parameterization Group Residual Block is designed in the CNN branch, which improves the local feature extraction ability through multi-scale and multi-branch structures, and the number of the Params and FLOPs are reduced effectively. The Neighborhood Enhancement Transformer Block is designed in the Transformer branch, in which the global attention is calculated by parallel Hadamard product and Dot product, and the global attention maps are enhanced by deep convolution operation to embed local features in global features. Thirdly, the Adaptive-random Mask Cropping method is designed to improve the features extraction ability for small lesions in CT images through heat map and random Mask techniques. RNE-DSNet is compared with other SOTA models. The Accuracy (ACC), Precision (PRE), Recall (REC), F1 score (F1), and Area Under Curve (AUC) values of RNE-DSNet are reached 99.22%, 99.23%, 99.08%, 99.23% and 99.38%, respectively, which verified the effectiveness of the RNE-DSNet model. The number Params and FLOPs are reduced by 62.78% and 84.20%, respectively. The effectiveness of RNE-DSNet is verified by heat map visualization. The performance of RNE-DSNet is generally better than the others methods. There is positive significance for computer aided diagnosis.

Real-time tracing and location for critical parts of moving objects with single-pixel detection.

A scenario based on single-pixel input of recognizing and tracing the critical parts of the moving targets has been proposed. The one-dimensional single-pixel matrix is achieved by bucket measurement by making use of pre-training illumination suitable for the target datasets. The designed recognition network can export the real-time position coordinates of the key components of the moving targets through single-pixel detection. Compared with the general random illumination light field, the results of the pre-training illumination show significant improvement in both recognition efficiency and accuracy. The recognition efficiency can reach 100Hz in the experimental detection of moving model planes. It may have important application prospects in military target tracking and hyperspectral remote sensing.

Human movement science-informed multi-task spatio temporal graph convolutional networks for fitness action recognition and evaluation

In recent years, with the rise of health consciousness, people’s demand for fitness has steadily increased. Utilizing automated human action recognition technology to monitor users’ movements during exercise continuously would help prevent situations where incorrect movements lead to injuries while working out. Skeleton-based human action recognition methods can overcome the susceptibility of past color-based and depth-based methods to various external backgrounds and noise, becoming a more successful solution in recent years. In this study, the auto-fitness advisor system we propose not only identifies the category of the action but also assesses the quality of the action and provides suggestions. We integrate human movement science, such as the Five Primary Kinetic Chains (5PKC), which defines the primary physiological principles in human movement, to enhance the accuracy of fitness action recognition by providing a more precise relationship between the human skeleton and muscles. For assessing the quality of movements and providing suggestions, we have designed a multi-task objective function within our model. Overall, the proposed model is a multi-task model based on Spatio Temporal Graph Convolutional Networks (ST-GCN), which employs the Five Primary Kinetic Chains (5PKC) as a partitioning strategy for skeletal information. In our experiments, we not only collected a certain amount of datasets in gyms to validate the performance of our model but also compared it with other current methods using existing public datasets.

Radar spectrum-image fusion using dual 2D-3D convolutional neural network to transformer inspired multi-headed self-attention bi-long short-term memory network for vehicle recognition

Radar spectrum-image fusion using dual 2D-3D convolutional neural network to transformer inspired multi-headed self-attention bi-long short-term memory network for vehicle recognition

Automated Laryngeal Invasion Detector of Boluses in Videofluoroscopic Swallowing Study Videos Using Action Recognition-Based Networks.

We aimed to develop an automated detector that determines laryngeal invasion during swallowing. Laryngeal invasion, which causes significant clinical problems, is defined as two or more points on the penetration-aspiration scale (PAS). We applied two three-dimensional (3D) stream networks for action recognition in videofluoroscopic swallowing study (VFSS) videos. To detect laryngeal invasion (PAS 2 or higher scores) in VFSS videos, we employed two 3D stream networks for action recognition. To establish the robustness of our model, we compared its performance with those of various current image classification-based architectures. The proposed model achieved an accuracy of 92.10%. Precision, recall, and F1 scores for detecting laryngeal invasion (≥PAS 2) in VFSS videos were 0.9470 each. The accuracy of our model in identifying laryngeal invasion surpassed that of other updated image classification models (60.58% for ResNet101, 60.19% for Swin-Transformer, 63.33% for EfficientNet-B2, and 31.17% for HRNet-W32). Our model is the first automated detector of laryngeal invasion in VFSS videos based on video action recognition networks. Considering its high and balanced performance, it may serve as an effective screening tool before clinicians review VFSS videos, ultimately reducing the burden on clinicians.

Unveiling SAR target recognition networks: Adaptive Perturbation Interpretation for enhanced understanding

Deep neural networks (DNNs) have obtained remarkable achievements in various vision tasks. However, DNNs’ mechanism remains obscure, especially in synthetic aperture radar (SAR) target recognition. To clearly understand DNNs’ mechanism, some interpretation algorithms are proposed to provide a saliency map to visualize DNN’s decisions, which can be categorized into (1) propagation-based methods, (2) activation-based methods, and (3) perturbation-based methods. Unfortunately, each of them has some imperfection in producing saliency maps. The saliency maps produced by propagation-based methods are sensitive to SAR speckles. Activation-based methods have difficulty producing a saliency map where the highlighted region covers the entire feature. Perturbation-based methods tend to forfeit intricate details of the target in saliency map during optimization. To mitigate the above limitations, this paper proposed Adaptive Perturbation Interpretation (API), with two aims: (1) to improve the interpretability of SAR recognition networks and (2) to recover the fine-grained lost during the optimization procedures of perturbation-based methods. Specifically, we first introduce a Multi-information Perturbation Optimization Module (MPOM) to retain the target’s local characteristics while constraining saliency map’s sparsity by configuring regularization terms. Additionally, we further introduce a Feature Compensation Module (FCM) to recover the fine-grained feature lost during the optimization process in MPOM. Extensive experiments are implemented on SAR interpretation benchmark datasets, MSTAR and SARAIRcraft1.0, the results demonstrate the superiority of API to other state-of-the-art methods.

Advanced Neural Networks for Software Data Classification and Recognition

Advanced Neural Networks for Software Data Classification and Recognition

MC-ShuffleNetV2: A lightweight model for maize disease recognition

Maize has a long history of cultivation and is renowned for its high yield, superior quality, and adaptability. Currently, maize holds a significant position in grain cultivation and occupies a significant place in the agricultural structure. However, maize is susceptible to various diseases during its growth process, which can have a significant impact on the quality and yield. Traditional machine learning is heavily reliant on feature extraction, whereas deep learning has demonstrated notable success in image recognition for computer vision.The use of bloated models and the resulting wastage of computational resources represent significant challenges. The paper proposes a lightweight model, MC-ShuffleNetV2 (Mish + Convolutional Block Attention Module + ShuffleNetV2), to meet the practical needs of convolutional neural networks in maize disease image recognition. The model has designed with a focus on network lightweighting and accurate feature extraction. The model was constructed upon the foundation of the high-performance ShuffleNetV2 1 × network. The Convolutional Block Attention Module was integrated into the network architecture to enhance the model’s adaptive expressiveness. The depthwise separable convolution kernel of the depth-separable module was modified from a 3 × 3 kernel to a 5 × 5 kernel. This modification was implemented with the objective of expanding the image receptive field and extracting more detailed features of the image. It was necessary to modify the activation function in each stage for Mish. The model was compressed through the application of pruning operations. In the maize disease dataset test, the accuracy of the test set recognition accuracy of the network model constructed in this paper reaches 99.86 %, the model parameters are only 873,936, and the FLOPs (Floating-point Operations) are only 1,751,286. Compared with LeNet, AlexNet, MobileNetV2, and EfficientNetV2 models, the MC-ShufflenetV2 model’s recognition ability and size have obvious advantages, and it is more conducive to the actual deployment of the agricultural mobile terminal.

Integrated Multi-Scale Aircraft Detection and Recognition with Scattering Point Intensity Adaptiveness in Complex Background Clutter SAR Images

Detecting aircraft targets in Synthetic Aperture Radar (SAR) images is critical for military and civilian applications. However, due to SAR’s special imaging mechanism, aircraft targets often consist of scattering points with large fluctuations in intensity. This often leads to the detector failing to detect weak scattering points. Not only that, previous SAR image aircraft-object-detection models have focused more on detecting and locating targets, with little emphasis on target recognition. This paper proposes a scattering-point-intensity-adaptive detection and recognition network (SADRN). In order to correctly detect the target area, we propose a Self-adaptive Bell-shaped Kernel (SBK) within the detector, which constructs a bell-shaped two-dimensional distribution centered on the target center, making the detection “threshold” for the target decrease from the center towards the periphery, reducing the missed alarms of weak scattering points at the edges of the target. To help the model adapt to multi-scale targets, we propose the FADLA-34 backbone network, aggregating information from feature maps across different scales. We also embed CBAM into the detector, which enhances the attention to the target area in the spatial dimension and strengthens the extraction of useful features in the channel dimension, reducing interference from the complex background clutter on object detection. Furthermore, to integrate detection and recognition, we introduce the multi-task head, which utilizes the three feature maps from the backbone network to generate the detection boxes and categories of the targets. Finally, the SADRN achieves superior detection and recognition performance on the SAR-AIRcraft-1.0, exceeding other mainstream methods. Visualization and analysis further confirm the effectiveness and superiority of the SADRN.

UYF-Net: A fusion network for feasible domain recognition and obstacle detection based on infrared thermography

Achieving safe navigation of unmanned surface vehicle (USV) in low light conditions remains a focus of research. The key to achieving the intelligent navigation of USV is the precise segmentation of navigable areas and fast obstacle detection. There are several challenges in processing infrared thermal imaging shoreline images using only semantic segmentation networks, including difficulties in extracting shorelines due to blurred details at the edges of infrared images, segmenting small targets, and complex obstacles due to small temperature differences in complex backgrounds. To address these issues, this paper introduces a novel UYF-Net network. The proposed method combines the improved U-Net with the YOLOv5m using a decision-level fusion strategy to achieve a better speed-accuracy tradeoff. Experimental results demonstrate that our method outperforms all comparative approaches discussed in this paper, further confirming the enhanced precision in segmentation, increased detection speed, and more stable model training process of the proposed network.

DACBN: Dual attention convolutional broad network for fine-grained visual recognition

Fine-grained visual classification (FGVC) is a challenging task due to its small inter-class differences and large intra-class differences. Most existing methods rely on manual labeling of key identification areas, which requires high labor costs. In addition, existing methods also tend to ignore the differences effect of different feature channels in the feature map, which has a certain impact on the model classification accuracy. To solve the above problems, this paper proposes a dual attention convolutional broad network. Firstly, a new dual attention mechanism is designed to suppress the background noise of fine-grained images and give greater weight to the discriminative feature regions and channels. Secondly, the ensemble broad learning system framework is used to further enhance the dual attention features, so that the discriminative features can further improve the recognition ability of the model. Finally, by multiple comparative experiments, it is reported that the method proposed in this article has achieved excellent recognition results on three commonly used datasets.

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images. To improve the CapsNet's performance, a Modified Version of Osprey Optimization Algorithm (MOA) has been utilized. Thesuggested approach has been tested on the ALL-IDB database, a widely recognized dataset for leukemia image classification. Comparative analysis with various machine learning techniques, including Combined combine MobilenetV2 and ResNet18 (MBV2/Res) network, Depth-wise convolution model, a hybrid model that combines a genetic algorithm with ResNet-50V2 (ResNet/GA), and SVM/JAYA demonstrated the superiority of our method in different terms. As a result, the proposed method is a robust and powerful tool for diagnosing leukemia from medical images.

LPI radar waveform recognition model based on multiple feature image and quasi-residual attention module

Abstract Detecting and classifying the modulation type of the intercepted noisy LPI radar waveform has become a hot topic in the field of Electronic Countermeasures (ECM). In this paper, we propose a recognition model based on multiple feature images (MFI) and convolutional neural networks (CNN) with the quasi-residual attention module (QRAM). The core technologies of this model are divided into two parts. One is the MFI that combines the ambiguity function image (AFI) of the complete signal, covariance matrix image (CMI) of the reconstructed signal, and short-time Fourier transform image (STFTI) of the truncated signal into RGB color image in signal processing as the input for recognition network. The other is CNN with a QRAM recognition network, in which the input is sliced into two mappings called identity mapping and residual mapping to build the quasi-residual module, and the attention module is embedded in each mapping to denoise and enhance the feature. The performance of the MFI-QRAM model is demonstrated by recognizing 12 modulation types of the signals defined in this paper. The simulation experiments show that the model has strong robustness with the number of train sets and the variation of SNR. The overall probability of successful recognition (PSR) is 96.17% when the SNR is −6 dB.

Pathological Priors Inspired Network for Vertebral Osteophytes Recognition.

Automatic vertebral osteophyte recognition in Digital Radiography is of great importance for the early prediction of degenerative disease but is still a challenge because of the tiny size and high inter-class similarity between normal and osteophyte vertebrae. Meanwhile, common sampling strategies applied in Convolution Neural Network could cause detailed context loss. All of these could lead to an incorrect positioning predicament. In this paper, based on important pathological priors, we define a set of potential lesions of each vertebra and propose a novel Pathological Priors Inspired Network (PPIN) to achieve accurate osteophyte recognition. PPIN comprises a backbone feature extractor integrating with a Wavelet Transform Sampling module for high-frequency detailed context extraction, a detection branch for locating all potential lesions and a classification branch for producing final osteophyte recognition. The Anatomical Map-guided Filter between two branches helps the network focus on the specific anatomical regions via the generated heatmaps of potential lesions in the detection branch to address the incorrect positioning problem. To reduce the inter-class similarity, a Bilateral Augmentation Module based on the graph relationship is proposed to imitate the clinical diagnosis process and to extract discriminative contextual information between adjacent vertebrae in the classification branch. Experiments on the two osteophytes-specific datasets collected from the public VinDr-Spine database show that the proposed PPIN achieves the best recognition performance among multitask frameworks and shows strong generalization. The results on a private dataset demonstrate the potential in clinical application. The Class Activation Maps also show the powerful localization capability of PPIN. The source codes are available in https://github.com/Phalo/PPIN.

Glimpse and Zoom: Spatio-Temporal Focused Dynamic Network for Skeleton-Based Action Recognition

Glimpse and Zoom: Spatio-Temporal Focused Dynamic Network for Skeleton-Based Action Recognition

Dual-Branch feature fusion residual neural network for individual radar signal recognition in low SNR environments

Abstract To address the challenge of radar emitter signal individual recognition, where single-dimensional radar fingerprint features are susceptible to noise interference and neural networks exhibit low recognition accuracy in low signal-to-noise ratio (SNR) environments, this study introduces a residual neural network predicated on a two-branch feature fusion. This approach amalgamates two-dimensional time-frequency domain features with one-dimensional intermediate-frequency signal features. Unlike existing algorithms, our proposed method integrates the knowledge gleaned from features learned across different dimensions. This integration enables the neural network to utilize features from multiple dimensions for recognition, thereby mitigating the impact of noise on a single feature. Experimental results demonstrate that our proposed algorithm outperforms others under various low SNRs, achieving an average recognition rate of 88.39%.