3D spatial measurement for model reconstruction: A review

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods.

/pdf/dynamic-edge-convolutional-neural-network-for-skeleton-based-2jqvdoin.pdf

Dynamic Edge Convolutional Neural Network for Skeleton-Based Human Action Recognition

Skeleton joint trajectories based human activity recognition using deep RNN

Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 by integrating new convolution operators into the Extended-Efficient Layer Aggregation Network for optimized feature extraction, reducing computations while capturing spatial features effectively. Secondly, to enhance the performance of the feature fusion network, we use Receptive Field Block as the feature pyramid of YOLOv7 and introduce Content-Aware ReAssembly of FEatures as upsampling operators for PRC-Light YOLO. By generating real-time adaptive convolution kernels, this module extends the receptive field, thereby gathering vital information from contexts with richer content. To further optimize the efficiency of model training, we apply the HardSwish activation function. Additionally, the bounding box loss function adopts the Wise-IOU v3, which incorporates a dynamic non-monotonic focusing mechanism that mitigates adverse gradients from low-quality instances. Finally, in order to enhance the PRC-Light YOLO model’s generalization ability, we apply data augmentation techniques to the fabric dataset. In comparison to the YOLOv7 model, multiple experiments indicate that our proposed fabric defect detection model exhibits a decrease of 18.03% in model parameters and 20.53% in computational load. At the same time, it has a notable 7.6% improvement in mAP.

PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection

Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection

Human action recognition based on enhanced data guidance and key node spatial temporal graph convolution

Multi-pose face reconstruction and Gabor-based dictionary learning for face recognition

Context receptive field and adaptive feature fusion for fabric defect detection

Fabric defect detection methods based on deep networks are widely used in the textile industry, but they often suffer from poor model generalization and blurry edge detection. To resolve these challenges, we propose a novel network called “U-SMR Net”, which integrates global contextual features, defect detail features, and high-level semantic features through the combination of ResNet-50 and Swin Transformer modules. Our U-SMR network includes a lightweight multiscale feature extraction module, the dual-branch pyramid Module (DBPM), which is nested to preserve high-resolution, shallow semantic information. We propose a recursive multi-level residual decoding block for multiscale fusion to refine, filter, and enhance input characteristics, generating prediction maps at multiple stages, and by employing an improved binary cross entropy loss function to supervise saliency mapping. The experimental results based on four groups from ZJU-Leaper dataset demonstrate the superior performance of our approach compared to other competitive methods by achieving an average fmeasure score of 75.33%, and finally testing results from both ZJU-Leaper-Total dataset and the HKU-Fabric dataset further support our U-SMR Net’s validity and generalization ability. 

U-SMR: U-SwinT & multi-residual network for fabric defect detection

This paper presents a textile defect detection method that utilizes a multi‐proportion spatial attention mechanism and channel memory feature fusion network by addressing the difficulties presented by complicated shapes and large size variations. In particular, a multi‐proportion spatial attention mechanism (MPAM) is introduced, which employs multi‐proportion convolution to improve the backbone network's capacity to detect non‐uniform structural defects. Additionally, the generality and adaptability of the model are enhanced by a multi‐scale spatial pyramid pooling structure (MS‐SPP). Second, a channel attention mechanism‐based memory feature fusion network is developed, which incorporates channel attention to adaptive weight the feature channels, focusing on crucial information channels to efficiently fuse contextual features and enhance the model's memory capacity. Finally, a novel efficient Wise‐IoU (EWIoU) loss function is proposed, which utilizes a dynamic non‐monotonic focusing mechanism to increase the penalty on distance measurement, thus enhancing the model's detection performance. Experiment findings on the ZJU‐Leaper and Tianchi textile datasets reveal that compared to the YOLOv7 baseline, the method in this paper has an increase of 6.5 and 2 percentage points, respectively, and the detection accuracy is better than most existing networks.

Textile defect detection based on multi‐proportion spatial attention mechanism and channel memory feature fusion network

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lan Di

Author Tools

Chat about Author