Multi-channel Feature Extraction Research Articles

AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection.

Simplified neural architecture for efficient human motion prediction in human-robot interaction

Human motion prediction is essential for safe and effective human-robot interaction, but modeling the intricate spatio-temporal dynamics inherent in human movement remains challenging. While recent methods have advanced motion prediction accuracy, they rely on complex neural architectures such as Graph Convolutional Networks and Recurrent Neural Networks, demanding extensive hyperparameter tuning. To overcome this limitation, we propose a streamlined multi-stage layer incremental perceptron (MSLP) architecture that achieves competitive results with far fewer parameters, improving efficiency. The MSLP eschews the complexity of prevalent networks and instead takes a stepped refinement approach to predict intricate motions using a lightweight model. This simplified yet effective design enables nuanced learning of spatio-temporal relationships without exhaustive tuning. Specifically, the MSLP incorporates two key components: a multi-channel feature extraction and enhancement block (MFE-block) and a temporal feature extraction module (TFE-block). The MFE-block strengthens the representation of each action node by integrating multi-dimensional action features. The TFE-block then captures the contextual relationships between actions over time. Together, the MFE-block and TFE-block allow the MSLP to model the complex spatial and temporal dynamics of human movement using a streamlined architecture and minimal parameter tuning. When evaluated on established datasets including Human3.6 M, CMU, 3DPW, and AMASS, the proposed MSLP method achieves improved accuracy gains of 16.7%-67.0% over existing state-of-the-art techniques. Additionally, the MSLP significantly reduces the number of parameters by 35.7%-99.5% compared to prior architectures. We find that the proposed MSLP significantly improves both long-term prediction and generalization capabilities of the model.

A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification

High-resolution synthetic aperture radars (SARs) are becoming an indispensable environmental monitoring system to capture the important geophysical phenomena on the earth and sea surface. However, there is a lack of comprehensive models that can orchestrate such large-scale datasets from numerous satellite missions such as GaoFen-3 and Sentinel-1. In addition, these SAR images of different ocean scenes need to convey a variety of high-level classification features in oceanic and atmospheric phenomena. In this study, we propose a multichannel neural network (MCNN) that supports oceanic SAR scene classification for limited oceanic data samples according to multi-feature fusion, data augmentation, and multichannel feature extraction. To exploit the multichannel semantics of SAR scenes, the multi-feature fusion module effectively combines and reshapes the spatiotemporal SAR images to preserve their structural properties. This fine-grained feature augmentation policy is extended to improve the data quality so that the classification model is less vulnerable to both small- and large-scale data. The multichannel feature extraction also aggregates different oceanic features convolutionally extracted from ocean SAR scenes to improve the classification accuracy of oceanic phenomena with different scales. Through extensive experimental analysis, our MCNN framework has demonstrated a commendable classification performance, achieving an average precision rate of 96%, an average recall rate of 95%, and an average F-score of 95% across ten distinct oceanic phenomena. Notably, it surpasses two state-of-the-art classification techniques, namely, AlexNet and CMwv, by margins of 23.7% and 18.3%, respectively.

Multi-channel feature extraction for virtual histological staining of photon absorption remote sensing images

Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications.

Detecting XSS with Random Forest and Multi-Channel Feature Extraction

Detecting XSS with Random Forest and Multi-Channel Feature Extraction

Underwater Image Enhancement Based on Color Feature Fusion

The ever-changing underwater environment, coupled with the complex degradation modes of underwater images, poses numerous challenges to underwater image enhancement efforts. Addressing the issues of low contrast and significant color deviations in underwater images, this paper presents an underwater image enhancement approach based on color feature fusion. By leveraging the properties of light propagation underwater, the proposed model employs a multi-channel feature extraction strategy, using convolution blocks of varying sizes to extract features from the red, green, and blue channels, thus effectively learning both global and local information of underwater images. Moreover, an attention mechanism is incorporated to design a residual enhancement module, augmenting the capability of feature representation. Lastly, a dynamic feature enhancement module is designed using deformable convolutions, enabling the network to capture underwater scene information with higher precision. Experimental results on public datasets demonstrate the outstanding performance of our proposed method in underwater image enhancement. Further, object detection experiments conducted on pre- and post-enhanced images underscore the value of our method for downstream tasks.

Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients

The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AICare outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships and variations in feature importance and provides reference values. An AI-doctor interaction system is developed for visualizing patients' health trajectories and risk indicators.

A Method of User Travel Mode Recognition Based on Convolutional Neural Network and Cell Phone Signaling Data

As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application.

FDA-MIMO Radar Target Recognition Based on SVM Classification with Multi-channel Feature Extraction

FDA-MIMO radar has good anti-active interference performance and has received extensive attention due to its distance dependence. However, there is still a lack of target and interference recognition methods for FDA-MIMO radar in complex environments. Therefore, in this paper, we propose a multi-channel feature extraction classification method based on the support vector machine (SVM), which realizes the classification of four active interferences and targets. In short, we classify the fine signal characteristics of different interference signals by extracting effective features and selecting efficient classifiers. Simulation results show that when the interference to noise ratio (INR) is greater than -15 dB, the recognition accuracy is greater than 0.95. It also shows that the proposed method can distinguish target and interference well in a variety of complex environments.

Recognition of Cervical Precancerous Lesions Based on Probability Distribution Feature Guidance.

Cervical cancer is a high incidence of cancer in women and cervical precancerous screening plays an important role in reducing the mortality rate. In this study, we proposed a multichannel feature extraction method based on the probability distribution features of the Acetowhite (AW) region to identify cervical precancerous lesions, with the overarching goal to improve the accuracy of cervical precancerous screening. A k-means clustering algorithm was first used to extract the cervical region images from the original colposcopy images. We then used a deep learning model called DeepLab V3+ to segment the AW region of the cervical image after the acetic acid experiment, from which the probability distribution map of the AW region after segmentation was obtained. This probability distribution map was fed into a neural network classification model for multichannel feature extraction, which resulted in the final classification performance. Results of the experimental evaluation showed that the proposed method achieved an average accuracy of 87.7%, an average sensitivity of 89.3%, and an average specificity of 85.6%. Compared with the methods that did not add segmented probability features, the proposed method increased the average accuracy rate, sensitivity, and specificity by 8.3%, 8%, and 8.4%, respectively. Overall, the proposed method holds great promise for enhancing the screening of cervical precancerous lesions in the clinic by providing the physician with more reliable screening results that might reduce their workload.

Variational Embedding Multiscale Diversity Entropy for Fault Diagnosis of Large-Scale Machinery

The large-scale machinery generally requires multiple accelerometers for condition monitoring. The multichannel vibration signals carry a wealth of fault information. Synchronous fault feature extraction using multichannel signals will significantly improve the diagnostic performance. The multivariate entropy method is able to synchronously extract the fault features from multiple sensors, but how to recognize the single fault occurring on different channels remains unexplored. In this article, we propose a novel feature extraction method called variational embedding multiscale diversity entropy, which constructs the phase space with different structures. The proposed variational phase space construction strategy will generate a different probability distribution for each channel, which results in a better separability for multichannel feature extraction. Combined with the random forest classifier, a novel fault diagnosis scheme is developed for condition monitoring of the large-scale machinery. One simulated signal and two experimental data are designed to validate the effectiveness of the proposed strategy. The results demonstrate that the proposed method has the best multichannel feature extraction ability compared with three existing methods: multivariate multiscale sample entropy, multivariate multiscale fuzzy entropy, and multivariate multiscale permutation entropy.

Multi-channel Feature Extraction and Super-Resolution Reconstruction of Remote Sensing Images

Super-resolution reconstruction is an imaging method to improve image resolution. It refers to reconstruct a clear high-resolution image from a low-resolution image. High-resolution remote sensing images can provide more detailed information and higher density, but in the field of remote sensing, because of the limitation of the hardware and vast distances, the remote sensing images are fuzzy sometimes. To facilitate subsequent tasks, this paper proposes a multi-channel feature extraction generative adversarial remote sensing image reconstruction method. According to the characteristics of remote sensing image, a generator is designed, which adds Laplace operator to enhance the edge information of the image, and uses multi-channel feature extraction, which not only enhances the ability of feature extraction but also reduces the number of parameters. In this paper, the super-resolution reconstruction task is carried out based on the 2X magnification factor, and the experimental results are evaluated on SET5/14 and NWPU-RESISC45 dataset. The experimental results show that the images generated by this method have a higher detailed texture and better super-resolution reconstruction effect of remote sensing images.

Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection

Fabric defect detection plays an important role in the quality control of textile products. Most existing defect detection techniques adopted traditional pattern recognition methods, which were lacking adaptability and presented the undesirable detection accuracy. In this paper, a fabric defect detection algorithm based on multi-channel feature matrixes extraction and joint low-rank decomposition was proposed by simulating biological visual perception mechanism. Based on the fact that the second-order gradient information is more suitable for characterizing the fabric texture, we developed a novel second-order multi-channel feature extraction method by modeling the response and distribution properties of the P-type ganglion cells in the primate retina. Upon devising a powerful descriptor, a joint low-rank decomposition method is utilized to model biological visual saliency, and decomposes the fabric images into backgrounds and salient defect objects. Experimental results demonstrate that our proposed algorithm has good self-adaptability and detection performance for plain and twill fabrics or complex patterned fabrics, and is superior to the state-of-the-art methods.

A novel dynamic update framework for epileptic seizure prediction.

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.