Enhance Feature Representation Research Articles

A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy [Formula: see text]-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

Innovative integration of multi-scale residual networks and MK-MMD for enhanced feature representation in fault diagnosis

This article proposes a method based on multi-scale expansion of residual neural networks (ResNets) to address challenges in the operation of rotating components, such as bearings and gears, under complex conditions where they are often affected by environmental noise. This interference leads to weaker fault characteristics, making feature selection difficult and increasing the presence of extraneous information features. To tackle this issue, the proposed method first employs a multi-scale feature ResNet to extract features from vibration signals of rotating machinery. The method decomposes the signal into multiple sub-signals of different scales, extracting local features at each scale. It then uses residual connections to combine these local features to obtain a global feature representation. Furthermore, the article introduces a construction of the maximum mean discrepancy (MMD) and minimization of entropy boundaries to adapt to the differences between two domains. The method utilizes multiple kernel functions to calculate distances between data at different scales and combines these distances to obtain a comprehensive measure. By employing the MMD and minimization of entropy boundary approach, the method can more accurately determine whether signals at different scales belong to the same category, thereby improving diagnostic accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed method in unsupervised cross-domain fault diagnosis tasks. Future work will focus on further optimizing the architecture of ResNets, enhancing feature extraction capabilities, and exploring advanced data augmentation methods to further improve the model’s generalization performance.

Surface Defect Detection of Aluminum Profiles Based on Multiscale and Self-Attention Mechanisms.

To address the various challenges in aluminum surface defect detection, such as multiscale intricacies, sensitivity to lighting variations, occlusion, and noise, this study proposes the AluDef-ClassNet model. Firstly, a Gaussian difference pyramid is utilized to capture multiscale image features. Secondly, a self-attention mechanism is introduced to enhance feature representation. Additionally, an improved residual network structure incorporating dilated convolutions is adopted to increase the receptive field, thereby enhancing the network's ability to learn from extensive information. A small-scale dataset of high-quality aluminum surface defect images is acquired using a CCD camera. To better tackle the challenges in surface defect detection, advanced deep learning techniques and data augmentation strategies are employed. To address the difficulty of data labeling, a transfer learning approach based on fine-tuning is utilized, leveraging prior knowledge to enhance the efficiency and accuracy of model training. In dataset testing, our model achieved a classification accuracy of 97.6%, demonstrating significant advantages over other classification models.

Fine grained food image recognition based on swin transformer

Fine-grained food image recognition is an important research direction in the field of computer vision and machine learning. However, fine-grained food image recognition faces huge challenges when dealing with foods that vary greatly in shape but belong to the same category or subcategories of that food. To improve this problem, this paper proposes a deep convolution module for obtaining local enhanced feature representation and combines it with the global feature representation obtained from Swin Transformer for deep residual, to obtain a deeper enhanced feature representation. An end-to-end fine-grained food universal classifier was also proposed, which can more accurately extract effective feature information from enhanced feature representations and achieve accurate recognition. Our approach can accurately handle foods with widely different shapes but belonging to the same category and is expected to help people better manage their diet and improve their health. Our models were trained and verified on the public fine-grained food datasets Foodx-251 and UEC Food-256 respectively, where the accuracy of the method on the validation set is 81.07% and 82.77% respectively. Compared with other state-of-the-art self-supervised methods, the method proposed in this paper exhibits higher accuracy in fine-grained food image recognition tasks.

CLRNet: A Cross Locality Relation Network for Crowd Counting in Videos.

In this article, we propose a new cross locality relation network (CLRNet) to generate high-quality crowd density maps for crowd counting in videos. Specifically, a cross locality relation module (CLRM) is proposed to enhance feature representations by modeling local dependencies of pixels between adjacent frames with an adapted local self-attention mechanism. First, different from the existing methods which measure similarity between pixels by dot product, a new adaptive cosine similarity is advanced to measure the relationship between two positions. Second, the traditional self-attention modules usually integrate the reconstructed features with the same weights for all the positions. However, crowd movement and background changes in a video sequence are uneven in real-life applications. As a consequence, it is inappropriate to treat all the positions in reconstructed features equally. To address this issue, a scene consistency attention map (SCAM) is developed to make CLRM pay more attention to the positions with strong correlations in adjacent frames. Furthermore, CLRM is incorporated into the network in a coarse-to-fine way to further enhance the representational capability of features. Experimental results demonstrate the effectiveness of our proposed CLRNet in comparison to the state-of-the-art methods on four public video datasets. The codes are available at: https://github.com/Amelie01/CLRNet.

Variational Attention-Based Interpretable Transformer Network for Rotary Machine Fault Diagnosis.

Deep learning technology provides a promising approach for rotary machine fault diagnosis (RMFD), where vibration signals are commonly utilized as input of a deep network model to reveal the internal state of machinery. However, most existing methods fail to mine association relationships within signals. Unlike deep neural networks, transformer networks are capable of capturing association relationships through the global self-attention mechanism to enhance feature representations from vibration signals. Despite this, transformer networks cannot explicitly establish the causal association between signal patterns and fault types, resulting in poor interpretability. To tackle these problems, an interpretable deep learning model named the variational attention-based transformer network (VATN) is proposed for RMFD. VATN is improved from transformer encoder to mine the association relationships within signals. To embed the prior knowledge of the fault type, which can be recognized based on several key features of vibration signals, a sparse constraint is designed for attention weights. Variational inference is employed to force attention weights to samples from Dirichlet distributions, and Laplace approximation is applied to realize reparameterization. Finally, two experimental studies conducted on bevel gear and bearing datasets demonstrate the effectiveness of VATN to other comparison methods, and the heat map of attention weights illustrates the causal association between fault types and signal patterns.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

Accuracy and efficiency stereo matching network with adaptive feature modulation.

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios.

Involution Transformer based U-Net for Landmark Detection in Ultrasound Images for Diagnosis of Infantile DDH.

The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants, which can conduct the Graf's method by detecting landmarks in hip ultrasound images. However, it is still necessary to explore more valuable information around these landmarks to enhance feature representation for improving detection performance in the detection model. To this end, a novel Involution Transformer based U-Net (IT-UNet) network is proposed for hip landmark detection. The IT-UNet integrates the efficient involution operation into Transformer to develop an Involution Transformer module (ITM), which consists of an involution attention block and a squeeze-and-excitation involution block. The ITM can capture both the spatial-related information and long-range dependencies from hip ultrasound images to effectively improve feature representation. Moreover, an Involution Downsampling block (IDB) is developed to alleviate the issue of feature loss in the encoder modules, which combines involution and convolution for the purpose of downsampling. The experimental results on two DDH ultrasound datasets indicate that the proposed IT-UNet achieves the best landmark detection performance, indicating its potential applications.

Graph Convolutional Network Discrete Hashing for Cross-Modal Retrieval.

With the rapid development of deep neural networks, cross-modal hashing has made great progress. However, the information of different types of data is asymmetrical, that is to say, if the resolution of an image is high enough, it can reproduce almost 100% of the real-world scenes. However, text usually carries personal emotion and it is not objective enough, so we generally think that the information of image will be much richer than text. Although most of the existing methods unify the semantic feature extraction and hash function learning modules for end-to-end learning, they ignore this issue and do not use information-rich modalities to support information-poor modalities, leading to suboptimal results, although they unify the semantic feature extraction and hash function learning modules for end-to-end learning. Furthermore, previous methods learn hash functions in a relaxed way that causes nontrivial quantization losses. To address these issues, we propose a new method called graph convolutional network (GCN) discrete hashing. This method uses a GCN to bridge the information gap between different types of data. The GCN can represent each label as word embedding, with the embedding regarded as a set of interdependent object classifiers. From these classifiers, we can obtain predicted labels to enhance feature representations across modalities. In addition, we use an efficient discrete optimization strategy to learn the discrete binary codes without relaxation. Extensive experiments conducted on three commonly used datasets demonstrate that our proposed method graph convolutional network-based discrete hashing (GCDH) outperforms the current state-of-the-art cross-modal hashing methods.

Deep learning cigarette defect detection method based on saliency feature guidance

AbstractCigarette defect detection is important in industrial production. Existing methods extract features for defect detection manually or using deep learning. However, due to the small size of cigarette defects, these methods are unable to effectively extract discriminative features, limiting detection performance. Hence, a deep learning‐based method called significant feature‐guided cigarette defect detection (SFGCD) is proposed, which combines saliency feature extraction methods with deep learning to enhance feature representation and improve detection. First, edge saliency features are extracted using the proposed target gradient saliency feature extraction (GSFE) strategy. Then, a dense multi‐level feature fusion network is designed to combine the original features with the saliency features obtained from the target gradient saliency feature extraction strategy. This network enriches feature representation and improves detection by fusing original and saliency features at different levels and scales. Experimental results demonstrate that the proposed method achieves a higher accuracy of 0.02 mean average precision (MAP) value and a detection speed of 5 frames per second (FPS) on the authors' own labeled cigarette defect dataset compared to existing state‐of‐the‐art methods.

Burst-Enhanced Super-Resolution Network (BESR).

Multi-frame super-resolution (MFSR) leverages complementary information between image sequences of the same scene to increase the resolution of the reconstructed image. As a branch of MFSR, burst super-resolution aims to restore image details by leveraging the complementary information between noisy sequences. In this paper, we propose an efficient burst-enhanced super-resolution network (BESR). Specifically, we introduce Geformer, a gate-enhanced transformer, and construct an enhanced CNN-Transformer block (ECTB) by combining convolutions to enhance local perception. ECTB efficiently aggregates intra-frame context and inter-frame correlation information, yielding an enhanced feature representation. Additionally, we leverage reference features to facilitate inter-frame communication, enhancing spatiotemporal coherence among multiple frames. To address the critical processes of inter-frame alignment and feature fusion, we propose optimized pyramid alignment (OPA) and hybrid feature fusion (HFF) modules to capture and utilize complementary information between multiple frames to recover more high-frequency details. Extensive experiments demonstrate that, compared to state-of-the-art methods, BESR achieves higher efficiency and competitively superior reconstruction results. On the synthetic dataset and real-world dataset of BurstSR, our BESR achieves PSNR values of 42.79 dB and 48.86 dB, respectively, outperforming other MFSR models significantly.

Small object detection algorithm incorporating swin transformer for tea buds.

Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identification remains an enormous challenge. Current methods based on traditional image processing and machine learning fail to effectively extract subtle features and morphology of small tea buds, resulting in low accuracy and robustness. To achieve accurate identification, this paper proposes a small object detection algorithm called STF-YOLO (Small Target Detection with Swin Transformer and Focused YOLO), which integrates the Swin Transformer module and the YOLOv8 network to improve the detection ability of small objects. The Swin Transformer module extracts visual features based on a self-attention mechanism, which captures global and local context information of small objects to enhance feature representation. The YOLOv8 network is an object detector based on deep convolutional neural networks, offering high speed and precision. Based on the YOLOv8 network, modules including Focus and Depthwise Convolution are introduced to reduce computation and parameters, increase receptive field and feature channels, and improve feature fusion and transmission. Additionally, the Wise Intersection over Union loss is utilized to optimize the network. Experiments conducted on a self-created dataset of tea buds demonstrate that the STF-YOLO model achieves outstanding results, with an accuracy of 91.5% and a mean Average Precision of 89.4%. These results are significantly better than other detectors. Results show that, compared to mainstream algorithms (YOLOv8, YOLOv7, YOLOv5, and YOLOx), the model improves accuracy and F1 score by 5-20.22 percentage points and 0.03-0.13, respectively, proving its effectiveness in enhancing small object detection performance. This research provides technical means for the accurate identification of small tea buds in complex environments and offers insights into small object detection. Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.

HF-YOLO: Advanced Pedestrian Detection Model with Feature Fusion and Imbalance Resolution

Pedestrian detection is crucial for various applications, including intelligent transportation and video surveillance systems. Although recent research has advanced pedestrian detection models like the YOLO series, they still face limitations in handling diverse pedestrian scales, leading to performance challenges. To address these issues, we propose HF-YOLO, an advanced pedestrian detection model. HF-YOLO tackles the complexities of pedestrian detection in complex scenes by addressing scale variations and occlusions among pedestrians. In the feature fusion stage, our algorithm leverages both shallow localization information and deep semantic information. This involves fusing P2 layer features and adding a high-resolution detection layer, significantly improving the detection of small-scale pedestrians and occluded instances. To enhance feature representation, HF-YOLO incorporates the HardSwish activation function, introducing more non-linear factors and strengthening the model’s ability to represent complex and discriminative features. Additionally, to address regression imbalance, a balance factor is introduced to the CIoU loss function. This modification effectively resolves the imbalance problem and enhances pedestrian localization accuracy. Experimental results demonstrate the effectiveness of our proposed algorithm. HF-YOLO achieves notable improvements, including a 3.52% increase in average precision, a 1.35% boost in accuracy, and a 4.83% enhancement in recall. Moreover, the algorithm maintains real-time performance with a detection time of 8.5ms, meeting the stringent requirements of real-time applications.

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder

BackgroundIntegrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse data.MethodsWe propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced AuToencoders (MOCAT) to utilize intra- and inter-omics information comprehensively. Additionally, attention mechanisms with confidence learning are incorporated for enhanced feature representation and trustworthy prediction.ResultsExtensive experiments were conducted on four benchmark datasets to evaluate the effectiveness of our proposed model, including BRCA, ROSMAP, LGG, and KIPAN. Our model significantly improved most evaluation measurements and consistently surpassed the state-of-the-art methods. Ablation studies showed that the auxiliary classifiers significantly boosted classification accuracy in the ROSMAP and LGG datasets. Moreover, the attention mechanisms and confidence evaluation block contributed to improvements in the predictive accuracy and generalizability of our model.ConclusionsThe proposed framework exhibits superior performance in disease classification and biomarker discovery, establishing itself as a robust and versatile tool for analyzing multi-layer biological data. This study highlights the significance of elaborated designed deep learning methodologies in dissecting complex disease phenotypes and improving the accuracy of disease predictions.

An encoder–decoder architecture with Fourier attention for chaotic time series multi-step prediction

Multi-step prediction of chaotic time series has been a challenging problem. An encoder–decoder architecture based on novel Fourier attention was proposed, called FGNet, applied to chaotic time series multi-step prediction. Two perspectives were carefully re-considered in modeling, one was the integrated use of frequency and temporal domain information, the other was the fusion of channel information and series information. The encoder was a self-attention architecture with Fourier attention module for capturing dynamic information in time series. Correspondingly, the decoder used the Gated Recurrent Unit (GRU) module with identity mapping, and the decoder was used to model the representation based on temporal dependency. For the interpretability deficiency of Fourier attention in FNet, which was proposed by google, a novel Fourier attention module was proposed. Specifically, the frequency domain features were extracted using Fourier transform, series and channel features were integrated capture through channel swapping coupling. Then these features were fused in the frequency domain space to enhance feature representation and their interpretability is achieved through inverse Fourier transform. Simulated data (Mackey–Glass system and Lorenz) and real data (sunspot time series) were used to verify that FGNet proposed achieves pleasantly surprising multi-step prediction performance in practice.

Advancing skin cancer diagnosis with a multi‐branch ShuffleNet architecture

AbstractIn this study, we present an innovative approach for enhancing skin cancer classification through a multi‐branch architecture inspired by ShuffleNet. Our methodology focuses on improving feature extraction and representation, emphasizing cross‐channel information exchange to achieve superior accuracy. The architecture comprises three branches: a primary feature enhancement branch, a parallel feature enhancement branch, and a global feature aggregation branch. We employ transposed convolution layers for upsampling, cross‐channel normalization, and the Swish activation function to enhance feature representations. Channel shuffle operations and group convolutions stimulate cross‐channel information exchange in both branches. The global feature aggregation branch utilizes global average pooling and depth concatenation to combine features from all branches. Subsequent Swish activation, followed by fully connected layers and softmax activation, yields class probabilities for precise skin cancer classification. This multi‐branch framework offers a promising avenue for accurate and informed medical image analysis in skin cancer detection. Integral to this research is the utilization of the ISIC2019 and ISIC 2020 datasets, encompassing diverse dermoscopic images from multiple sources. By leveraging these datasets, the methodology capitalizes on comprehensive data for precise skin cancer detection, thereby advancing medical image analysis.

SFE-Net: Spatial-Frequency Enhancement Network for robust nuclei segmentation in histopathology images

Morphological features of individual nuclei serve as a dependable foundation for pathologists in making accurate diagnoses. Existing methods that rely on spatial information for feature extraction have achieved commendable results in nuclei segmentation tasks. However, these approaches are not sufficient to extract edge information of nuclei with small sizes and blurred outlines. Moreover, the lack of attention to the interior of the nuclei leads to significant internal inconsistencies. To address these challenges, we introduce a novel Spatial-Frequency Enhancement Network (SFE-Net) to incorporate spatial-frequency features and promote intra-nuclei consistency for robust nuclei segmentation. Specifically, SFE-Net incorporates a distinctive Spatial-Frequency Feature Extraction module and a Spatial-Guided Feature Enhancement module, which are designed to preserve spatial-frequency information and enhance feature representation respectively, to achieve comprehensive extraction of edge information. Furthermore, we introduce the Label-Guided Distillation method, which utilizes semantic features to guide the segmentation network in strengthening boundary constraints and learning the intra-nuclei consistency of individual nuclei, to improve the robustness of nuclei segmentation. Extensive experiments on three publicly available histopathology image datasets (MoNuSeg, TNBC and CryoNuSeg) demonstrate the superiority of our proposed method, which achieves 79.23%, 81.96% and 73.26% Aggregated Jaccard Index, respectively. The proposed model is available at https://github.com/jinshachen/SFE-Net.

CLG: Contrastive Label Generation with Knowledge for Few-Shot Learning

Training large-scale models needs big data. However, the few-shot problem is difficult to resolve due to inadequate training data. It is valuable to use only a few training samples to perform the task, such as using big data for application scenarios due to cost and resource problems. So, to tackle this problem, we present a simple and efficient method, contrastive label generation with knowledge for few-shot learning (CLG). Specifically, we: (1) Propose contrastive label generation to align the label with data input and enhance feature representations; (2) Propose a label knowledge filter to avoid noise during injection of the explicit knowledge into the data and label; (3) Employ label logits mask to simplify the task; (4) Employ multi-task fusion loss to learn different perspectives from the training set. The experiments demonstrate that CLG achieves an accuracy of 59.237%, which is more than about 3% in comparison with the best baseline. It shows that CLG obtains better features and gives the model more information about the input sentences to improve the classification ability.

Location and segmentation of important railway signs based on improved segmentation

Purpose With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection. Design/methodology/approach This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure. Findings This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs. Research limitations/implications The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate. Social implications The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security. Originality/value This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.