Segmentation Dataset Research Articles

Automated Machine Vision Inspection System for Applying Plastic Lids on Wet Wipes Packs

Computer vision techniques have been widely used in industrial manufacturing for automation, monitoring, quality assessment and inspection. Specifically, some techniques are being deployed to detect and identify products’ defects instead of human operators. In this paper, a vision system is proposed to inspect and assess the quality of applying plastic lids on wet wipes packs at a manufacturing company in Jordan. Currently, the manufacturing company uses automatic lid applicator that has no visual control system and thus, a human operator is needed to inspect every pack. This process includes detecting the position and orientation of each lid to assure that it has been applied correctly at the center of the pack above specific label. Automating this inspection process would save time and efforts and increase the productivity. The proposed inspection system has the following modules: (1) Pack and lid detection and segmentation using YOLOv8s-Seg algorithm; (2) distance to border (DtB) extraction between the pack centroid and lid boundary; and (3) inspection module using linear Support Vector Machine (SVM). A segmentation dataset of 319 different images of wet wipes packs was constructed using the Segment Anything Model (SAM). The proposed inspection has been tested on different wet wipes packs. Experimental results successfully demonstrated the efficiency of the inspection system.

HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation.

Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discriminative; i.e., they aim to learn a mapping from the input image to segmentation masks. However, these discriminative methods neglect the underlying data distribution and intrinsic class characteristics, suffering from unstable feature space. In this work, we propose to complement discriminative segmentation methods with the knowledge of underlying data distribution from generative models. To that end, we propose a novel hybrid diffusion framework for medical image segmentation, termed HiDiff, which can synergize the strengths of existing discriminative segmentation models and new generative diffusion models. HiDiff comprises two key components: discriminative segmentor and diffusion refiner. First, we utilize any conventional trained segmentation models as discriminative segmentor, which can provide a segmentation mask prior for diffusion refiner. Second, we propose a novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which can effectively, efficiently, and interactively refine the segmentation mask by modeling the underlying data distribution. Third, we train the segmentor and BBDM in an alternate-collaborative manner to mutually boost each other. Extensive experimental results on abdomen organ, brain tumor, polyps, and retinal vessels segmentation datasets, covering four widely-used modalities, demonstrate the superior performance of HiDiff over existing medical segmentation algorithms, including the state-of-the-art transformer- and diffusion-based ones. In addition, HiDiff excels at segmenting small objects and generalizing to new datasets. Source codes are made available at https://github.com/takimailto/HiDiff.

Continuous-time Object Segmentation using High Temporal Resolution Event Camera.

Event cameras are novel bio-inspired sensors, where individual pixels operate independently and asynchronously, generating intensity changes as events. Leveraging the microsecond resolution (no motion blur) and high dynamic range (compatible with extreme light conditions) of events, there is considerable promise in directly segmenting objects from sparse and asynchronous event streams in various applications. However, different from the rich cues in video object segmentation, it is challenging to segment complete objects from the sparse event stream. In this paper, we present the first framework for continuous-time object segmentation from event stream. Given the object mask at the initial time, our task aims to segment the complete object at any subsequent time in event streams. Specifically, our framework consists of a Recurrent Temporal Embedding Extraction (RTEE) module based on a novel ResLSTM, a Cross-time Spatiotemporal Feature Modeling (CSFM) module which is a transformer architecture with long-term and short-term matching modules, and a segmentation head. The historical events and masks (reference sets) are recurrently fed into our framework along with current-time events. The temporal embedding is updated as new events are input, enabling our framework to continuously process the event stream. To train and test our model, we construct both real-world and simulated event-based object segmentation datasets, each comprising event streams, APS images, and object annotations. Extensive experiments on our datasets demonstrate the effectiveness of the proposed recurrent architecture. Our code and dataset are available at https://sites.google.com/view/ecos-net/.

LViT: Language Meets Vision Transformer in Medical Image Segmentation.

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

Robust Fine-Grained Visual Recognition With Neighbor-Attention Label Correction.

Existing deep learning methods for fine-grained visual recognition often rely on large-scale, well-annotated training data. Obtaining fine-grained annotations in the wild typically requires concentration and expertise, such as fine category annotation for species recognition, instance annotation for person re-identification (re-id) and dense annotation for segmentation, which inevitably leads to label noise. This paper aims to tackle label noise in deep model training for fine-grained visual recognition. We propose a Neighbor-Attention Label Correction (NALC) model to correct labels during the training stage. NALC samples a training batch and a validation batch from the training set. It hence leverages a meta-learning framework to correct labels in the training batch based on the validation batch. To enhance the optimization efficiency, we introduce a novel nested optimization algorithm for the meta-learning framework. The proposed training procedure consistently improves label accuracy in the training batch, consequently enhancing the learned image representation. Experimental results demonstrate that our method significantly increases label accuracy from 70% to over 98% and outperforms recent approaches by up to 13.4% in mean Average Precision (mAP) on various fine-grained image retrieval (FGIR) tasks, including instance retrieval on CUB200 and person re-id on Market1501. We also demonstrate the efficacy of NALC on noisy semantic segmentation datasets generated from Cityscapes, where it achieves a significant 7.8% improvement in mIOU score. NALC also exhibits robustness to different types of noise, including simulated noise such as Asymmetric, Pair-Flip, and Pattern noise, as well as practical noisy labels generated by tracklets and clustering.

Semi-Supervised Semantic Segmentation for Light Field Images Using Disparity Information.

Light field (LF) images enable numerous applications due to their ability to capture information for multiple views. Semantic segmentation is an essential task for LF scene understanding. However, existing supervised methods heavily rely on a large number of pixel-wise annotations. To relieve this problem, we propose a semi-supervised LF semantic segmentation method that requires only a small subset of labeled data and harnesses the LF disparity information. First, we design an unsupervised disparity estimation network, which can determine the disparity map for every view. With the estimated disparity maps, we generate pseudo-labels along with their weight maps for the peripheral views when only the labels of central views are available. We then merge the predictions from multiple views to obtain more reliable pseudo-labels for unlabeled data, and introduce a disparity-semantics consistency loss to enforce structure similarity. Moreover, we develop a comprehensive contrastive learning scheme that includes a pixel-level strategy to enhance feature representations and an object-level strategy to improve segmentation for individual objects. Our method demonstrates state-of-the-art performance on the benchmark LF semantic segmentation dataset under a variety of training settings and achieves comparable performance to supervised methods when trained under 1/2 protocol.

Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images.

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://github.com/poiuohke/SASR_Net.

Towards Safer Highway Work Zones: Insights from Deep Learning Analysis of Thermal Footage

Thermal imaging, when coupled with deep-learning-based analysis, can significantly improve highway work zone management and safety, though data scarcity in this field has been a historical challenge. We collected over 440 hours of thermal footage from highway sites in Massachusetts and New Hampshire and created a vehicle segmentation dataset featuring over 14,000 vehicle instances. We implement effective training strategies for deep learning models and develop algorithms to analyze vehicle trajectories and merging behaviors. Various visualization techniques are illustrated to better understand the extensive analytics data generated for each site, which can lead to development of data-driven strategies to improve Traffic management and safety. GitHub repository: https://github.com/z00bean/SmartWorkZoneControl.

Towards Connectivity-Aware Pulmonary Airway Segmentation.

Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral pulmonary lesions. Convolutional Neural Networks (CNNs) are promising for automated analysis of medical imaging, which however performs poorly on airway segmentation. Specifically, breakage of small bronchi distals cannot be effectively eliminated in the prediction results of CNNs, which is detrimental to use as a reference for bronchoscopic-assisted surgery. In this paper, we proposed a connectivity-aware segmentation framework to improve the performance of airway segmentation. A Connectivity-Aware Surrogate (CAS) module is first proposed to balance the training progress within-class distribution. Furthermore, a Local-Sensitive Distance (LSD) module is designed to identify the breakage and minimize the variation of the distance map between the prediction and ground-truth. The proposed method is validated with the publically available reference airway segmentation datasets. The detected rate of branch and length on public EXACT'09 and BAS datasets are 82.1%/79.6% and 96.5%/91.5% respectively, demonstrating the effectiveness of the method in terms of improving the connectedness of the segmentation performance. The source code is available at: https://github.com/Puzzled-Hui/Connectivity-Aware-Airway-Segmentation.

Gradient-Guided Network With Fourier Enhancement for Glioma Segmentation in Multimodal 3D MRI.

Glioma segmentation is a crucial task in computer-aided diagnosis, requiring precise discrimination between lesions and normal tissue at the pixel level. Popular methods neglect crucial edge information, leading to inaccurate contour delineation. Moreover, global information has been proven beneficial for segmentation. The feature representations extracted by convolution neural networks often struggle with local-related information owing to the limited receptive fields. To address these issues, we propose a novel edge-aware segmentation network that incorporates a dual-path gradient-guided training strategy with Fourier edge-enhancement for precise glioma segmentation, a.k.a. GFNet. First, we introduce a Dual-path Gradient-guided Training strategy (DGT) based on a Siamese network guiding the optimizing direction of one path by the gradient from the other path. DGT pays attention to the indistinguishable pixels with large weight-updating gradient, such as the pixels near the boundary, to guide the network training, addressing hard samples. Second, to further perceive the edge information, we derive a Fourier Edge-enhancement Module (FEM) to augment feature edges with high-frequency representations from the spectral domain, providing global information and edge details. Extensive experiments on public glioma segmentation datasets, BraTS2020 and Medical Segmentation Decathlon (MSD) glioma and prostate segmentation, demonstrate that GFNet achieves competitive performance compared to other state-of-the-art methods, both qualitatively and quantitatively.

PL-Net: progressive learning network for medical image segmentation.

In recent years, deep convolutional neural network-based segmentation methods have achieved state-of-the-art performance for many medical analysis tasks. However, most of these approaches rely on optimizing the U-Net structure or adding new functional modules, which overlooks the complementation and fusion of coarse-grained and fine-grained semantic information. To address these issues, we propose a 2D medical image segmentation framework called Progressive Learning Network (PL-Net), which comprises Internal Progressive Learning (IPL) and External Progressive Learning (EPL). PL-Net offers the following advantages: 1) IPL divides feature extraction into two steps, allowing for the mixing of different size receptive fields and capturing semantic information from coarse to fine granularity without introducing additional parameters; 2) EPL divides the training process into two stages to optimize parameters and facilitate the fusion of coarse-grained information in the first stage and fine-grained information in the second stage. We conducted comprehensive evaluations of our proposed method on five medical image segmentation datasets, and the experimental results demonstrate that PL-Net achieves competitive segmentation performance. It is worth noting that PL-Net does not introduce any additional learnable parameters compared to other U-Net variants.

Inter-Class and Inter-Domain Semantic Augmentation for Domain Generalization.

The domain generalization approach seeks to develop a universal model that performs well on unknown target domains with the aid of diverse source domains. Data augmentation has proven to be an effective method to enhance domain generalization in computer vision. Recently, semantic-level based data augmentation has yielded remarkable results. However, these methods focus on sampling semantic directions on feature space from intra-class and intra-domain, limiting the diversity of the source domain. To address this issue, we propose a novel approach called Inter-Class and Inter-Domain Semantic Augmentation (CDSA) for domain generalization. We first introduce a sampling-based method called CrossSmooth to obtain semantic directions from inter-class. Then, CrossVariance obtains the styles of different domains by sampling semantic directions. Our experiments on four well-known domain generalization benchmark datasets (Digits-DG, PACS, Office-Home, and DomainNet) demonstrate the effectiveness of our approach. We also validate our approach on commonly-used semantic segmentation datasets, namely GTAV, SYNTHIA, Cityscapes, Mapillary, and BDDS which also show significant improvements.

Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named "soft mask", which has richer and more accurate edge details description and better visualization, and develop a universal automatic soft mask annotation pipeline to deal with different datasets correspondingly. Then, a novel network with detailed representation transfer and soft mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special detailed representation transfer module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images and an adversarial training framework with soft mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation, and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies (https://drive.google.com/file/d/15NNkvDTb_0Ku0IoPsNMHezJR TH1Oi1wm/view?usp=sharing).

Multi-ConDoS: Multimodal Contrastive Domain Sharing Generative Adversarial Networks for Self-Supervised Medical Image Segmentation.

Existing self-supervised medical image segmentation usually encounters the domain shift problem (i.e., the input distribution of pre-training is different from that of fine-tuning) and/or the multimodality problem (i.e., it is based on single-modal data only and cannot utilize the fruitful multimodal information of medical images). To solve these problems, in this work, we propose multimodal contrastive domain sharing (Multi-ConDoS) generative adversarial networks to achieve effective multimodal contrastive self-supervised medical image segmentation. Compared to the existing self-supervised approaches, Multi-ConDoS has the following three advantages: (i) it utilizes multimodal medical images to learn more comprehensive object features via multimodal contrastive learning; (ii) domain translation is achieved by integrating the cyclic learning strategy of CycleGAN and the cross-domain translation loss of Pix2Pix; (iii) novel domain sharing layers are introduced to learn not only domain-specific but also domain-sharing information from the multimodal medical images. Extensive experiments on two publicly multimodal medical image segmentation datasets show that, with only 5% (resp., 10%) of labeled data, Multi-ConDoS not only greatly outperforms the state-of-the-art self-supervised and semi-supervised medical image segmentation baselines with the same ratio of labeled data, but also achieves similar (sometimes even better) performances as fully supervised segmentation methods with 50% (resp., 100%) of labeled data, which thus proves that our work can achieve superior segmentation performances with very low labeling workload. Furthermore, ablation studies prove that the above three improvements are all effective and essential for Multi-ConDoS to achieve this very superior performance.

The Performance of two Well-Known Segmentation Convolutional Neural Networks, Unet and Segnet, for the Segmentation of Blood Vessels, Optic Disc and Demarcation Line in Retinopathy of Prematurity Retcam Images

Background: Screening is the predominant strategy for early detection of ROP. However, image analysis is dependent on the experience of ophthalmologists, which introduces subjective variables into the interpretation of data. Computer-aided diagnostics has recently become more widely employed as a second-reader tool, reducing doctors' diagnostic uncertainties. Based on this, the development of such systems is critical, and it may be done by refining its processes, namely, more accurate segmentation and disease detection. Given this circumstance, the current study presents the use of two well-known convolutional neural networks (CNNs) developed for segmentation, U-Net and SegNet, to segment blood vessels (BV), optic discs (OD), and demarcation lines/ridges in ROP fundus images. Defining which architecture is most suited for such a task can assist in reducing the stress on ophthalmologists for ROP screenings and triage. Methods: CNNs are used in segmentation to classify each pixel in an image using self-trained weights. Using three data subsets, each containing 50 RetCam images of ROP and their corresponding masks regarding the BV, OD, or ridge of interest, retrieved from the only publicly available ROP segmentation dataset, i.e. HVDROPDB, we compared the automatic segmentation performed by the U-Net and SegNet using different configurations and in different segmentation tasks related to ROP diagnosis. Results: Among the two proposed architectures, U-Net obtained better overall results in all segmentation tasks, obtaining accuracies of 0.933-0.8751, dice coefficients of 0.49-0.648, and took less training time than the SegNet, which achieved accuracies of 0.933-0.80 and dice scores of 0.40-0.648. Conclusion: The two networks can segment RetCam images of ROP with useful accuracy depending on their configuration, with U-Net being generally faster to train and more accurate.

Strategies and Practices of Intelligent Imputation in Data Mining Based on Contact Number Evaluation

Abstract This paper proposes the general architecture of a multi-scale classification system based on the intelligent imputation method of multi-scale classification. SVM is used to transform the storage structure model of the object, obtain the trained support vector set with corresponding weights, measure the similarity between two data points based on the Hausdorff distance, realize the construction of a similarity matrix, adopt the idea of mean value, blur the data information, and improve the mechanism of on-scale imputation. The bicubic difference method is used as the theoretical basis of scale-down extrapolation, and the scale-down extrapolation algorithm DAMSC is established. Based on the quaternionic linkage number, the dataset data are preprocessed, and the data eigenvalue weights are calculated at the same time. The multi-scale classification validity index evaluates the model’s accuracy in classification and its performance in both upward and downward scale extrapolation. When the categorized data is 7, the MSCVI metric of the model attains a peak of 0.92254 in the segmentation dataset run results, which is more advantageous than other metrics. In the regression test of the model’s imputation results, the correlation coefficient of bus passengers is 0.99, the adjusted R-square is 0.97043, and the coefficient of the regression equation is 1.042, and the model in this paper is valid and reliable for the imputation of passenger drop-off points.

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contrast and high noise in the images. While numerous scholars have introduced deep learning-based CT image segmentation algorithms, they still face challenges, particularly in achieving high edge accuracy and addressing pixel classification errors. To tackle these issues, this study proposes the MIS-Net (Medical Images Segment Net) model, a deep learning-based approach. The MIS-Net model incorporates multi-scale atrous convolution into the encoding and decoding structure with symmetry, enabling the comprehensive extraction of multi-scale features from CT images. This enhancement aims to improve the accuracy of lung and liver edge segmentation. In the evaluation using the COVID-19 CT Lung and Infection Segmentation dataset, the left and right lung segmentation results demonstrate that MIS-Net achieves a Dice Similarity Coefficient (DSC) of 97.61. Similarly, in the Liver Tumor Segmentation Challenge 2017 public dataset, the DSC of MIS-Net reaches 98.78.

GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery

In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.

MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation.

Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases. However, the availability of algorithms for automatic segmentation of honeycomb lung lesions remains limited. In this study, we propose a novel multi-scale cross-layer attention fusion network (MCAFNet) specifically designed for the segmentation of honeycomb lung lesions, taking into account their shape specificity and similarity to surrounding vascular shadows. The MCAFNet incorporates several key modules to enhance the segmentation performance. Firstly, a multiscale aggregation (MIA) module is introduced in the input part to preserve spatial information during downsampling. Secondly, a cross-layer attention fusion (CAF) module is proposed to capture multiscale features by integrating channel information and spatial information from different layers of the feature maps. Lastly, a bidirectional attention gate (BAG) module is constructed within the skip connection to enhance the model's ability to filter out background information and focus on the segmentation target. Experimental results demonstrate the effectiveness of the proposed MCAFNet. On the honeycomb lung segmentation dataset, the network achieves an Intersection over Union (IoU) of 0.895, mean IoU (mIoU) of 0.921, and mean Dice coefficient (mDice) of 0.949, outperforming existing medical image segmentation algorithms. Furthermore, experiments conducted on additional datasets confirm the generalizability and robustness of the proposed model. The contribution of this study lies in the development of the MCAFNet, which addresses the lack of automated segmentation algorithms for honeycomb lung lesions. The proposed network demonstrates superior performance in accurately segmenting honeycomb lung lesions, thereby facilitating the diagnosis and treatment of lung diseases. This work contributes to the existing literature by presenting a novel approach that effectively combines multi-scale features and attention mechanisms for lung lesion segmentation. The code is available at https://github.com/Oran9er/MCAFNet .

EFDCNet: Encoding fusion and decoding correction network for RGB-D indoor semantic segmentation

Semantic segmentation is a crucial task in vision measurement systems that involves understanding and segmenting different objects and regions within an image. Over the years, numerous RGB-D semantic segmentation methods have been developed, leveraging the encoder-decoder architecture to achieve outstanding performance. However, existing methods have two main problems that constrain further performance improvement. Firstly, in the encoding stage, existing methods have a weak ability to fuse cross-modal information, and low-quality depth maps can easily lead to poor feature representation. Secondly, in the decoding stage, the upsampling of high-level semantic information may cause the loss of contextual information, and low-level features from the encoder may bring noises to the decoder through skip connections. To solve these issues, we propose a novel Encoding Fusion and Decoding Correction Network (EFDCNet) for RGB-D indoor semantic segmentation. First, in the encoding stage of EFDCNet, we focus on extracting valuable information from low-quality depth maps, and employ a channel-wise filter to select informative depth features. Additionally, we establish the global dependencies between RGB and depth features via the self-attention mechanism to enhance the cross-modal feature interactions, extracting discriminant and powerful features. Then, in the decoding stage of EFDCNet, we use the highest-level information as semantic guidance to compensate for the upsampling information and filter out noise from the low-level encoder features propagated through the skip connections to the decoder. Extensive experiments conducted on two widely-used RGB-D indoor semantic segmentation datasets demonstrate that the proposed EFDCNet surpasses the performance of relevant state-of-the-art methods. The code is available at https://github.com/ Mark9010/EFDCNet