IET Computer Vision最新文献_第7页

Cascading AB-YOLOv5 and PB-YOLOv5 for rib fracture detection in frontal and oblique chest X-ray images 级联AB-YOLOv5和PB-YOLOv 5在胸部正位和斜位X射线图像中检测肋骨骨折

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-28 DOI: 10.1049/cvi2.12239

Hsin-Chun Tsai, Nan-Han Lu, Kuo-Ying Liu, Chuan-Han Lin, Jhing-Fa Wang

{"title":"Cascading AB-YOLOv5 and PB-YOLOv5 for rib fracture detection in frontal and oblique chest X-ray images","authors":"Hsin-Chun Tsai, Nan-Han Lu, Kuo-Ying Liu, Chuan-Han Lin, Jhing-Fa Wang","doi":"10.1049/cvi2.12239","DOIUrl":"https://doi.org/10.1049/cvi2.12239","url":null,"abstract":"Convolutional deep learning models have shown comparable performance to radiologists in detecting and classifying thoracic diseases. However, research on rib fractures remains limited compared to other thoracic abnormalities. Moreover, existing deep learning models primarily focus on using frontal chest X-ray (CXR) images. To address these gaps, the authors utilised the EDARib-CXR dataset, comprising 369 frontal and 829 oblique CXRs. These X-rays were annotated by experienced radiologists, specifically identifying the presence of rib fractures using bounding-box-level annotations. The authors introduce two detection models, AB-YOLOv5 and PB-YOLOv5, and train and evaluate them on the EDARib-CXR dataset. AB-YOLOv5 is a modified YOLOv5 network that incorporates an auxiliary branch to enhance the resolution of feature maps in the final convolutional network layer. On the other hand, PB-YOLOv5 maintains the same structure as the original YOLOv5 but employs image patches during training to preserve features of small objects in downsampled images. Furthermore, the authors propose a novel two-level cascaded architecture that integrates both AB-YOLOv5 and PB-YOLOv5 detection models. This structure demonstrates improved metrics on the test set, achieving an AP30 score of 0.785. Consequently, the study successfully develops deep learning-based detectors capable of identifying and localising fractured ribs in both frontal and oblique CXR images.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"17 7","pages":"750-762"},"PeriodicalIF":1.7,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50146880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IDBNet: Improved differentiable binarisation network for natural scene text detection IDBNet：用于自然场景文本检测的改进型可微分二值化网络

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-28 DOI: 10.1049/cvi2.12241

Zhijia Zhang, Yiming Shao, Ligang Wang, Haixing Li, Yunpeng Liu

{"title":"IDBNet: Improved differentiable binarisation network for natural scene text detection","authors":"Zhijia Zhang, Yiming Shao, Ligang Wang, Haixing Li, Yunpeng Liu","doi":"10.1049/cvi2.12241","DOIUrl":"10.1049/cvi2.12241","url":null,"abstract":"The text in the natural scene can express rich semantic information, which helps people understand and analyse daily things. This paper focuses on the problems of discrete text spatial distribution and variable text geometric size in natural scenes with complex backgrounds and proposes an end-to-end natural scene text detection method based on DBNet. The authors first use IResNet as the backbone network, which does not increase network parameters while retaining more text features. Furthermore, a module with Transformer is introduced in the feature extraction stage to strengthen the correlation between high-level feature pixels. Then, the authors add a spatial pyramid pooling structure in the end of feature extraction, which realises the combination of local and global features, enriches the expressive ability of feature maps, and alleviates the detection limitations caused by the geometric size of features. Finally, to better integrate the features of each level, a dual attention module is embedded after multi-scale feature fusion. Extensive experiments on the MSRA-TD500, CTW1500, ICDAR2015, and MLT2017 data set are conducted. The results showed that IDBNet can improve the average precision, recall, and F-measure of a text compared with the state of art text detection methods and has higher predictive ability and practicability.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 2","pages":"224-235"},"PeriodicalIF":1.7,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135425628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time vehicle detection using segmentation-based detection network and trajectory prediction 利用基于分割的检测网络和轨迹预测进行实时车辆检测

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-25 DOI: 10.1049/cvi2.12236

Nafiseh Zarei, Payman Moallem, Mohammadreza Shams

{"title":"Real-time vehicle detection using segmentation-based detection network and trajectory prediction","authors":"Nafiseh Zarei, Payman Moallem, Mohammadreza Shams","doi":"10.1049/cvi2.12236","DOIUrl":"10.1049/cvi2.12236","url":null,"abstract":"The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u-net-based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA-DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 2","pages":"191-209"},"PeriodicalIF":1.7,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135864622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continuous sign language recognition based on hierarchical memory sequence network 基于分层记忆序列网络的连续手语识别

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-22 DOI: 10.1049/cvi2.12240

Cuihong Xue, Jingli Jia, Ming Yu, Gang Yan, Yingchun Guo, Yuehao Liu

{"title":"Continuous sign language recognition based on hierarchical memory sequence network","authors":"Cuihong Xue, Jingli Jia, Ming Yu, Gang Yan, Yingchun Guo, Yuehao Liu","doi":"10.1049/cvi2.12240","DOIUrl":"10.1049/cvi2.12240","url":null,"abstract":"With the goal of solving the problem of feature extractors lacking strong supervision training and insufficient time information concerning single-sequence model learning, a hierarchical sequence memory network with a multi-level iterative optimisation strategy is proposed for continuous sign language recognition. This method uses the spatial-temporal fusion convolution network (STFC-Net) to extract the spatial-temporal information of RGB and Optical flow video frames to obtain the multi-modal visual features of a sign language video. Then, in order to enhance the temporal relationships of visual feature maps, the hierarchical memory sequence network is used to capture local utterance features and global context dependencies across time dimensions to obtain sequence features. Finally, the decoder decodes the final sentence sequence. In order to enhance the feature extractor, the authors adopted a multi-level iterative optimisation strategy to fine-tune STFC-Net and the utterance feature extractor. The experimental results on the RWTH-Phoenix-Weather multi-signer 2014 dataset and the Chinese sign language dataset show the effectiveness and superiority of this method.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 2","pages":"247-259"},"PeriodicalIF":1.7,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136062314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scene flow estimation from 3D point clouds based on dual-branch implicit neural representations 基于双分支隐式神经表征的三维点云场景流估计

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-15 DOI: 10.1049/cvi2.12237

Mingliang Zhai, Kang Ni, Jiucheng Xie, Hao Gao

{"title":"Scene flow estimation from 3D point clouds based on dual-branch implicit neural representations","authors":"Mingliang Zhai, Kang Ni, Jiucheng Xie, Hao Gao","doi":"10.1049/cvi2.12237","DOIUrl":"10.1049/cvi2.12237","url":null,"abstract":"Recently, online optimisation-based scene flow estimation has attracted significant attention due to its strong domain adaptivity. Although online optimisation-based methods have made significant advances, the performance is far from satisfactory as only flow priors are considered, neglecting scene priors that are crucial for the representations of dynamic scenes. To address this problem, the authors introduce a dual-branch MLP-based architecture to encode implicit scene representations from a source 3D point cloud, which can additionally synthesise a target 3D point cloud. Thus, the mapping function between the source and synthesised target 3D point clouds is established as an extra implicit regulariser to capture scene priors. Moreover, their model infers both flow and scene priors in a stronger bidirectional manner. It can effectively establish spatiotemporal constraints among the synthesised, source, and target 3D point clouds. Experiments on four challenging datasets, including KITTI scene flow, FlyingThings3D, Argoverse, and nuScenes, show that our method can achieve potential and comparable results, proving its effectiveness and generality.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 2","pages":"210-223"},"PeriodicalIF":1.7,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain-invariant attention network for transfer learning between cross-scene hyperspectral images 用于跨场景高光谱图像间转移学习的域不变注意力网络

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-15 DOI: 10.1049/cvi2.12238

Minchao Ye, Chenglong Wang, Zhihao Meng, Fengchao Xiong, Yuntao Qian

{"title":"Domain-invariant attention network for transfer learning between cross-scene hyperspectral images","authors":"Minchao Ye, Chenglong Wang, Zhihao Meng, Fengchao Xiong, Yuntao Qian","doi":"10.1049/cvi2.12238","DOIUrl":"https://doi.org/10.1049/cvi2.12238","url":null,"abstract":"Small-sample-size problem is always a challenge for hyperspectral image (HSI) classification. Considering the co-occurrence of land-cover classes between similar scenes, transfer learning can be performed, and cross-scene classification is deemed a feasible approach proposed in recent years. In cross-scene classification, the source scene which possesses sufficient labelled samples is used for assisting the classification of the target scene that has a few labelled samples. In most situations, different HSI scenes are imaged by different sensors resulting in their various input feature dimensions (i.e. number of bands), hence heterogeneous transfer learning is desired. An end-to-end heterogeneous transfer learning algorithm namely domain-invariant attention network (DIAN) is proposed to solve the cross-scene classification problem. The DIAN mainly contains two modules. (1) A feature-alignment CNN (FACNN) is applied to extract features from source and target scenes, respectively, aiming at projecting the heterogeneous features from two scenes into a shared low-dimensional subspace. (2) A domain-invariant attention block is developed to gain cross-domain consistency with a specially designed class-specific domain-invariance loss, thus further eliminating the domain shift. The experiments on two different cross-scene HSI datasets show that the proposed DIAN achieves satisfying classification results.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"17 7","pages":"739-749"},"PeriodicalIF":1.7,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50151176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A temporal shift reconstruction network for compressive video sensing 用于压缩视频传感的时移重构网络

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-09-09 DOI: 10.1049/cvi2.12234

Zhenfei Gu, Chao Zhou, Guofeng Lin

{"title":"A temporal shift reconstruction network for compressive video sensing","authors":"Zhenfei Gu, Chao Zhou, Guofeng Lin","doi":"10.1049/cvi2.12234","DOIUrl":"10.1049/cvi2.12234","url":null,"abstract":"Compressive sensing provides a promising sampling paradigm for video acquisition for resource-limited sensor applications. However, the reconstruction of original video signals from sub-sampled measurements is still a great challenge. To exploit the temporal redundancies within videos during the recovery, previous works tend to perform alignment on initial reconstructions, which are too coarse to provide accurate motion estimations. To solve this problem, the authors propose a novel reconstruction network, named TSRN, for compressive video sensing. Specifically, the authors utilise a number of stacked temporal shift reconstruction blocks (TSRBs) to enhance the initial reconstruction progressively. Each TSRB could learn the temporal structures by exchanging information with last and next time step, and no additional computations is imposed on the network compared to regular 2D convolutions due to the high efficiency of temporal shift operations. After the enhancement, a bidirectional alignment module to build accurate temporal dependencies directly with the help of optical flows is employed. Different from previous methods that only extract supplementary information from the key frames, the proposed alignment module can receive temporal information from the whole video sequence via bidirectional propagations, thus yielding better performance. Experimental results verify the superiority of the proposed method over other state-of-the-art approaches quantitatively and qualitatively.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 4","pages":"448-457"},"PeriodicalIF":1.7,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STFT: Spatial and temporal feature fusion for transformer tracker STFT：变压器跟踪器的时空特征融合

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-08-31 DOI: 10.1049/cvi2.12233

Hao Zhang, Yan Piao, Nan Qi

{"title":"STFT: Spatial and temporal feature fusion for transformer tracker","authors":"Hao Zhang, Yan Piao, Nan Qi","doi":"10.1049/cvi2.12233","DOIUrl":"10.1049/cvi2.12233","url":null,"abstract":"Siamese-based trackers have demonstrated robust performance in object tracking, while Transformers have achieved widespread success in object detection. Currently, many researchers use a hybrid structure of convolutional neural networks and Transformers to design the backbone network of trackers, aiming to improve performance. However, this approach often underutilises the global feature extraction capability of Transformers. The authors propose a novel Transformer-based tracker that fuses spatial and temporal features. The tracker consists of a multilayer spatial feature fusion network (MSFFN), a temporal feature fusion network (TFFN), and a prediction head. The MSFFN includes two phases: feature extraction and feature fusion, and both phases are constructed with a Transformer. Compared with the hybrid structure of “CNNs + Transformer,” the proposed method enhances the continuity of feature extraction and the ability of information interaction between features, enabling comprehensive feature extraction. Moreover, to consider the temporal dimension, the authors propose a TFFN for updating the template image. The network utilises the Transformer to fuse the tracking results of multiple frames with the initial frame, allowing the template image to continuously incorporate more information and maintain the accuracy of target features. Extensive experiments show that the tracker STFT achieves state-of-the-art results on multiple benchmarks (OTB100, VOT2018, LaSOT, GOT-10K, and UAV123). Especially, the tracker STFT achieves remarkable area under the curve score of 0.652 and 0.706 on the LaSOT and OTB100 benchmark respectively.","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"18 1","pages":"165-176"},"PeriodicalIF":1.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42381518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A latent topic-aware network for dense video captioning 一种用于密集视频字幕的潜在主题感知网络

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-08-29 DOI: 10.1049/cvi2.12195

Tao Xu, Yuanyuan Cui, Xinyu He, Caihua Liu

引用次数: 0

Feature fusion over hyperbolic graph convolution networks for video summarisation 用于视频摘要的双曲图卷积网络特征融合

IF 1.7 4区计算机科学

IET Computer Vision Pub Date : 2023-08-25 DOI: 10.1049/cvi2.12232

GuangLi Wu, ShengTao Wang, ShiPeng Xu

引用次数: 0