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Article ; Online: Operational reliability assessment considering independency between wind power ramps and frequency sensitive loads

Xiao Xiaolong / Gao Qingxu / Han Xiaoqing / Liang Dingkang

E3S Web of Conferences, Vol 242, p

2021 Volume 03005

Abstract: With the increasing wind power penetration, power system frequency is more vulnerable especially when gust wind periods. Frequency control is used to restore system frequency back to the rated value. But, during frequency control processes, demand of ... ...

Abstract	With the increasing wind power penetration, power system frequency is more vulnerable especially when gust wind periods. Frequency control is used to restore system frequency back to the rated value. But, during frequency control processes, demand of frequency sensitive load (FSL) is different from that of frequency-insensitive load and changes with the change in system frequency. It is necessary to consider load demand combined with dynamic frequency deviation. This paper proposed a new reliability evaluation method of generation systems based on blade-element theory and frequency control method. The blade-element theory is utilized to improve the evaluation accuracy of wind power output in the presence of wind power ramp events (WPREs). The frequency control considering dynamic demand of FSL is used to mimic frequency regulation processes and establish reliability evaluation model. Case studies are presented using wind data from a wind farm in Shanxi Providence, and the results suggest that reliability problems caused by FSL can’t be ignored in some cases.
Keywords	Environmental sciences ; GE1-350
Subject code	600
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	EDP Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: DDS3D

Li, Jingyu / Liu, Zhe / Hou, Jinghua / Liang, Dingkang

Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection

2023

Abstract: In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the ... ...

Abstract	In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision information for the student network. On the other hand, instead of traditional fixed thresholds, we propose a dynamic threshold manner to generate pseudo-labels, which can guarantee the quality and quantity of pseudo-labels during the whole training process. Benefiting from these two components, our DDS3D outperforms the state-of-the-art semi-supervised 3d object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist under the same configuration of 1% samples. Extensive ablation studies on the KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models will be made publicly available at https://github.com/hust-jy/DDS3D Comment: Accepted for publication in 2023 IEEE International Conference on Robotics and Automation (ICRA)
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-03-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Cell Localization and Counting Using Direction Field Map.

Chen, Yajie / Liang, Dingkang / Bai, Xiang / Xu, Yongchao / Yang, Xin

IEEE journal of biomedical and health informatics

2022 Volume 26, Issue 1, Page(s) 359–368

Abstract: Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then use ... ...

Abstract	Automatic cell counting in pathology images is challenging due to blurred boundaries, low-contrast, and overlapping between cells. In this paper, we train a convolutional neural network (CNN) to predict a two-dimensional direction field map and then use it to localize cell individuals for counting. Specifically, we define a direction field on each pixel in the cell regions (obtained by dilating the original annotation in terms of cell centers) as a two-dimensional unit vector pointing from the pixel to its corresponding cell center. Direction field for adjacent pixels in different cells have opposite directions departing from each other, while those in the same cell region have directions pointing to the same center. Such unique property is used to partition overlapped cells for localization and counting. To deal with those blurred boundaries or low contrast cells, we set the direction field of the background pixels to be zeros in the ground-truth generation. Thus, adjacent pixels belonging to cells and background will have an obvious difference in the predicted direction field. To further deal with cells of varying density and overlapping issues, we adopt geometry adaptive (varying) radius for cells of different densities in the generation of ground-truth direction field map, which guides the CNN model to separate cells of different densities and overlapping cells. Extensive experimental results on three widely used datasets (i.e., VGG Cell, CRCHistoPhenotype2016, and MBM datasets) demonstrate the effectiveness of the proposed approach.
MeSH term(s)	Humans ; Neural Networks, Computer
Language	English
Publishing date	2022-01-17
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2021.3105545
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: An End-to-End Transformer Model for Crowd Localization

Liang, Dingkang / Xu, Wei / Bai, Xiang

2022

Abstract: Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. ...

Abstract	Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end Crowd Localization Transformer named CLTR that solves the task in the regression-based paradigm. The proposed method views the crowd localization as a direct set prediction problem, taking extracted features and trainable embeddings as input of the transformer-decoder. To reduce the ambiguous points and generate more reasonable matching results, we introduce a KMO-based Hungarian matcher, which adopts the nearby context as the auxiliary matching cost. Extensive experiments conducted on five datasets in various data settings show the effectiveness of our method. In particular, the proposed method achieves the best localization performance on the NWPU-Crowd, UCF-QNRF, and ShanghaiTech Part A datasets. Comment: Accepted by ECCV 2022. The project page is at https://dk-liang.github.io/CLTR/
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-02-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Comprehensive Benchmark Datasets for Amharic Scene Text Detection and Recognition

Dikubab, Wondimu / Liang, Dingkang / Liao, Minghui / Bai, Xiang

2022

Abstract: Ethiopic/Amharic script is one of the oldest African writing systems, which serves at least 23 languages (e.g., Amharic, Tigrinya) in East Africa for more than 120 million people. The Amharic writing system, Abugida, has 282 syllables, 15 punctuation ... ...

Abstract	Ethiopic/Amharic script is one of the oldest African writing systems, which serves at least 23 languages (e.g., Amharic, Tigrinya) in East Africa for more than 120 million people. The Amharic writing system, Abugida, has 282 syllables, 15 punctuation marks, and 20 numerals. The Amharic syllabic matrix is derived from 34 base graphemes/consonants by adding up to 12 appropriate diacritics or vocalic markers to the characters. The syllables with a common consonant or vocalic markers are likely to be visually similar and challenge text recognition tasks. In this work, we presented the first comprehensive public datasets named HUST-ART, HUST-AST, ABE, and Tana for Amharic script detection and recognition in the natural scene. We have also conducted extensive experiments to evaluate the performance of the state of art methods in detecting and recognizing Amharic scene text on our datasets. The evaluation results demonstrate the robustness of our datasets for benchmarking and its potential of promoting the development of robust Amharic script detection and recognition algorithms. Consequently, the outcome will benefit people in East Africa, including diplomats from several countries and international communities. Comment: 2 pages 1 figure 1 supplementary document
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2022-03-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

Liang, Dingkang / Xu, Wei / Zhu, Yingying / Zhou, Yu

2021

Abstract: In this paper, we propose a novel map for dense crowd localization and crowd counting. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving significant progress recently. However, these regression- ... ...

Abstract	In this paper, we propose a novel map for dense crowd localization and crowd counting. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving significant progress recently. However, these regression-based methods are often unable to provide a precise location for each person, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map. To tackle this issue, we propose a novel Focal Inverse Distance Transform (FIDT) map for crowd localization and counting. Compared with the density maps, the FIDT maps accurately describe the people's location, without overlap between nearby heads in dense regions. We simultaneously implement crowd localization and counting by regressing the FIDT map. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art localization-based methods in crowd localization tasks, achieving very competitive performance compared with the regression-based methods in counting tasks. In addition, the proposed method presents strong robustness for the negative samples and extremely dense scenes, which further verifies the effectiveness of the FIDT map. The code and models are available at https://github.com/dk-liang/FIDTM. Comment: The code and models are available at https://github.com/dk-liang/FIDTM
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2021-02-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Visual Information Extraction in the Wild

Kuang, Jianfeng / Hua, Wei / Liang, Dingkang / Yang, Mingkun / Jiang, Deqiang / Ren, Bo / Bai, Xiang

Practical Dataset and End-to-end Solution

2023

Abstract: Visual information extraction (VIE), which aims to simultaneously perform OCR and information extraction in a unified framework, has drawn increasing attention due to its essential role in various applications like understanding receipts, goods, and ... ...

Abstract	Visual information extraction (VIE), which aims to simultaneously perform OCR and information extraction in a unified framework, has drawn increasing attention due to its essential role in various applications like understanding receipts, goods, and traffic signs. However, as existing benchmark datasets for VIE mainly consist of document images without the adequate diversity of layout structures, background disturbs, and entity categories, they cannot fully reveal the challenges of real-world applications. In this paper, we propose a large-scale dataset consisting of camera images for VIE, which contains not only the larger variance of layout, backgrounds, and fonts but also much more types of entities. Besides, we propose a novel framework for end-to-end VIE that combines the stages of OCR and information extraction in an end-to-end learning fashion. Different from the previous end-to-end approaches that directly adopt OCR features as the input of an information extraction module, we propose to use contrastive learning to narrow the semantic gap caused by the difference between the tasks of OCR and information extraction. We evaluate the existing end-to-end methods for VIE on the proposed dataset and observe that the performance of these methods has a distinguishable drop from SROIE (a widely used English dataset) to our proposed dataset due to the larger variance of layout and entities. These results demonstrate our dataset is more practical for promoting advanced VIE algorithms. In addition, experiments demonstrate that the proposed VIE method consistently achieves the obvious performance gains on the proposed and SROIE datasets. Comment: 15 pages, 6 figures, ICDAR2023
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-05-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: CrowdCLIP

Liang, Dingkang / Xie, Jiahao / Zou, Zhikang / Ye, Xiaoqing / Xu, Wei / Bai, Xiang

Unsupervised Crowd Counting via Vision-Language Model

2023

Abstract: Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is ... ...

Abstract	Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented impressive performance on various downstream tasks; 2) there is a natural mapping between crowd patches and count text. To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem. Specifically, in the training stage, we exploit the multi-modal ranking loss by constructing ranking text prompts to match the size-sorted crowd patches to guide the image encoder learning. In the testing stage, to deal with the diversity of image patches, we propose a simple yet effective progressive filtering strategy to first select the highly potential crowd patches and then map them into the language space with various counting intervals. Extensive experiments on five challenging datasets demonstrate that the proposed CrowdCLIP achieves superior performance compared to previous unsupervised state-of-the-art counting methods. Notably, CrowdCLIP even surpasses some popular fully-supervised methods under the cross-dataset setting. The source code will be available at https://github.com/dk-liang/CrowdCLIP. Comment: Accepted by CVPR 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-04-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: SOOD

Hua, Wei / Liang, Dingkang / Li, Jingyu / Liu, Xiaolong / Zou, Zhikang / Ye, Xiaoqing / Bai, Xiang

Towards Semi-Supervised Oriented Object Detection

2023

Abstract: Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that ... ...

Abstract	Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://github.com/HamPerdredes/SOOD. Comment: Accepted to CVPR 2023. Code will be available at https://github.com/HamPerdredes/SOOD
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-04-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Discrepancy Aware Framework for Robust Anomaly Detection

Cai, Yuxuan / Liang, Dingkang / Luo, Dongliang / He, Xinwei / Yang, Xin / Bai, Xiang

2023

Abstract: Defect detection is a critical research area in artificial intelligence. Recently, synthetic data-based self-supervised learning has shown great potential on this task. Although many sophisticated synthesizing strategies exist, little research has been ... ...

Abstract	Defect detection is a critical research area in artificial intelligence. Recently, synthetic data-based self-supervised learning has shown great potential on this task. Although many sophisticated synthesizing strategies exist, little research has been done to investigate the robustness of models when faced with different strategies. In this paper, we focus on this issue and find that existing methods are highly sensitive to them. To alleviate this issue, we present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies across different anomaly detection benchmarks. We hypothesize that the high sensitivity to synthetic data of existing self-supervised methods arises from their heavy reliance on the visual appearance of synthetic data during decoding. In contrast, our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. To this end, inspired by existing knowledge distillation methods, we employ a teacher-student network, which is trained based on synthesized outliers, to compute the discrepancy map as the cue. Extensive experiments on two challenging datasets prove the robustness of our method. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance. Code is available at: https://github.com/caiyuxuan1120/DAF. Comment: Accepted by IEEE Transactions on Industrial Informatics. Code is available at: https://github.com/caiyuxuan1120/DAF
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-10-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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