Sensors

Research

19 pages, 5950 KiB

Open AccessArticle

Ancient Chinese Character Recognition with Improved Swin-Transformer and Flexible Data Enhancement Strategies

by Yi Zheng, Yi Chen, Xianbo Wang, Donglian Qi and Yunfeng Yan

Sensors 2024, 24(7), 2182; https://doi.org/10.3390/s24072182 - 28 Mar 2024

Viewed by 607

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat [...] Read more.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

17 pages, 6501 KiB

Open AccessArticle

Unsupervised Conditional Diffusion Models in Video Anomaly Detection for Monitoring Dust Pollution

by Limin Cai, Mofei Li and Dianpeng Wang

Sensors 2024, 24(5), 1464; https://doi.org/10.3390/s24051464 - 23 Feb 2024

Viewed by 646

Abstract

Video surveillance is widely used in monitoring environmental pollution, particularly harmful dust. Currently, manual video monitoring remains the predominant method for analyzing potential pollution, which is inefficient and prone to errors. In this paper, we introduce a new unsupervised method based on latent [...] Read more.

Video surveillance is widely used in monitoring environmental pollution, particularly harmful dust. Currently, manual video monitoring remains the predominant method for analyzing potential pollution, which is inefficient and prone to errors. In this paper, we introduce a new unsupervised method based on latent diffusion models. Specifically, we propose a spatio-temporal network structure, which better integrates the spatial and temporal features of videos. Our conditional guidance mechanism samples frames of input videos to guide high-quality generation and obtains frame-level anomaly scores, comparing generated videos with original ones. We also propose an efficient compression strategy to reduce computational costs, allowing the model to perform in a latent space. The superiority of our method was demonstrated by numerical experiments in three public benchmarks and practical application analysis in coal mining over previous SOTA methods with better AUC, of at most over 3%. Our method accurately detects abnormal patterns in multiple challenging environmental monitoring scenarios, illustrating the potential application possibilities in the environmental protection domain and beyond. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

35 pages, 29002 KiB

Open AccessArticle

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

by José Miguel Monzón-Verona, Pablo González-Domínguez and Santiago García-Alonso

Sensors 2024, 24(4), 1317; https://doi.org/10.3390/s24041317 - 18 Feb 2024

Viewed by 707

Abstract

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated [...] Read more.

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

21 pages, 18333 KiB

Open AccessArticle

Parsing Netlists of Integrated Circuits from Images via Graph Attention Network

by Wenxing Hu, Xianke Zhan and Minglei Tong

Sensors 2024, 24(1), 227; https://doi.org/10.3390/s24010227 - 30 Dec 2023

Viewed by 1157

Abstract

A massive number of paper documents that include important information such as circuit schematics can be converted into digital documents by optical sensors like scanners or digital cameras. However, extracting the netlists of analog circuits from digital documents is an exceptionally challenging task. [...] Read more.

A massive number of paper documents that include important information such as circuit schematics can be converted into digital documents by optical sensors like scanners or digital cameras. However, extracting the netlists of analog circuits from digital documents is an exceptionally challenging task. This process aids enterprises in digitizing paper-based circuit diagrams, enabling the reuse of analog circuit designs and the automatic generation of datasets required for intelligent design models in this domain. This paper introduces a bottom-up graph encoding model aimed at automatically parsing the circuit topology of analog integrated circuits from images. The model comprises an improved electronic component detection network based on the Swin Transformer, an algorithm for component port localization, and a graph encoding model. The objective of the detection network is to accurately identify component positions and types, followed by automatic dataset generation through port localization, and finally, utilizing the graph encoding model to predict potential connections between circuit components. To validate the model’s performance, we annotated an electronic component detection dataset and a circuit diagram dataset, comprising 1200 and 3552 training samples, respectively. Detailed experimentation results demonstrate the superiority of our proposed enhanced algorithm over comparative algorithms across custom and public datasets. Furthermore, our proposed port localization algorithm significantly accelerates the annotation speed of circuit diagram datasets. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

21 pages, 8353 KiB

Open AccessArticle

Velocity and Color Estimation Using Event-Based Clustering

by Xavier Lesage, Rosalie Tran, Stéphane Mancini and Laurent Fesquet

Sensors 2023, 23(24), 9768; https://doi.org/10.3390/s23249768 - 11 Dec 2023

Viewed by 817

Abstract

Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms similar event groups while simultaneously [...] Read more.

Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms similar event groups while simultaneously estimating their attributes. This work proposes taking advantage of additional event information in order to provide new attributes for further processing. We elaborate on the estimation of the object velocity using the mean motion of the cluster. Next, we are examining a novel form of events, which includes intensity measurement of the color at the concerned pixel. These events may be processed to estimate the rough color of a cluster, or the color distribution in a cluster. Lastly, this paper presents some applications that utilize these features. The resulting algorithms are applied and exercised thanks to a custom event-based simulator, which generates videos of outdoor scenes. The velocity estimation methods provide satisfactory results with a trade-off between accuracy and convergence speed. Regarding color estimation, the luminance estimation is challenging in the test cases, while the chrominance is precisely estimated. The estimated quantities are adequate for accurately classifying objects into predefined categories. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

15 pages, 7876 KiB

Open AccessArticle

Detection of AI-Created Images Using Pixel-Wise Feature Extraction and Convolutional Neural Networks

by Fernando Martin-Rodriguez, Rocio Garcia-Mojon and Monica Fernandez-Barciela

Sensors 2023, 23(22), 9037; https://doi.org/10.3390/s23229037 - 8 Nov 2023

Viewed by 2896

Abstract

Generative AI has gained enormous interest nowadays due to new applications like ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a text prompt and are even able to [...] Read more.

Generative AI has gained enormous interest nowadays due to new applications like ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a text prompt and are even able to create photorealistic images. Due to this fact, intense research has been performed to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deepfake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research is to detect photorealistic AI-created images versus real photos coming from a physical camera. Id est, making a binary decision over an image, asking whether it is artificially or naturally created. Artificial images do not need to try to represent any real object, person, or place. For this purpose, techniques that perform a pixel-level feature extraction are used. The first one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. The second one is error level analysis (ELA). This is another type of feature extraction traditionally used for detecting image editing. ELA is being used nowadays by photographers for the manual detection of AI-created images. Both kinds of features are used to train convolutional neural networks to differentiate between AI images and real photographs. Good results are obtained, achieving accuracy rates of over 95%. Both extraction methods are carefully assessed by computing precision/recall and F₁-score measurements. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

16 pages, 5659 KiB

Open AccessArticle

Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5

by Xiaomei Yi, Cheng Qian, Peng Wu, Brian Tapiwanashe Maponde, Tengteng Jiang and Wenying Ge

Sensors 2023, 23(19), 8204; https://doi.org/10.3390/s23198204 - 30 Sep 2023

Cited by 3 | Viewed by 1350

Abstract

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took [...] Read more.

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model’s accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

13 pages, 300 KiB

Open AccessArticle

Display-Semantic Transformer for Scene Text Recognition

by Xinqi Yang, Wushour Silamu, Miaomiao Xu and Yanbing Li

Sensors 2023, 23(19), 8159; https://doi.org/10.3390/s23198159 - 28 Sep 2023

Viewed by 1307

Abstract

Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some [...] Read more.

Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some noisy (distorted and blurry, etc.) images. In order to address the aforementioned issues, this study builds upon the most recent findings of the Vision Transformer, and our approach (called Display-Semantic Transformer, or DST for short) constructs a masked language model and a semantic visual interaction module. The model can mine deep semantic information from images to assist scene text recognition and improve the robustness of the model. The semantic visual interaction module can better realize the interaction between semantic information and visual features. In this way, the visual features can be enhanced by the semantic information so that the model can achieve a better recognition effect. The experimental results show that our model improves the average recognition accuracy on six benchmark test sets by nearly 2% compared to the baseline. Our model retains the benefits of having a small number of parameters and allows for fast inference speed. Additionally, it attains a more optimal balance between accuracy and speed. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Image Processing and Pattern Recognition Based on Deep Learning—2nd Edition

Share This Special Issue

Special Issue Editors

Special Issue Information

Related Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI