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Boundary refinement network with semantic embedding connections for UAV aerial image semantic segmentation

JOURNAL OF ELECTRONIC IMAGING 2023年第6期32卷 063003-063003页

作者： Li, Runzeng Shi, Zaifeng Kong, Fanning Zhao, Xiangyang Luo, Tao Tianjin Univ Sch Microelect Tianjin Peoples R China Tianjin Key Lab Imaging & Sensing Microelect Techn Tianjin Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

Deep-learning-based semantic segmentation is the research focus for unmanned aerospace vehicle (UAV) aerial images analysis. However, there are problems in segmenting small and narrow objects and boundary regions, due to the large size differences between objects and the unbalanced class data in aerial images. A network named SEC-BRNet is proposed for the boundary refinement problem. First, the semantic embedding connections and progressive upsampling decoder are used to obtain spatial details for generating fused feature maps, which are then concatenated in decoding process level by level for recovering the boundary details. Second, a multiloss training strategy is developed for data imbalance and boundary roughness problems, including cross-entropy loss, Dice loss, and active boundary loss. After extensive experiments, our network could achieve 84.8% mIoU and 89.04% Boundary IoU on the AeroScapes dataset and achieve 62.81% mIoU and 90.78% Boundary IoU on the Semantic Drone Dataset. The experimental results indicate that the proposed SEC-BRNet performs well in semantic segmentation task for UAV aerial images.

关键词： semantic segmentation UAV aerial image boundary refinement encoder-decoder multiloss training

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A sequence-to-sequence model for joint bridge response forecasting

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期203卷

作者： Bahrami, Omid Wang, Wentao Hou, Rui Lynch, Jerome P. Univ Michigan Depart Civil & Env Engn Ann Arbor MI 48109 USA Duke Univ Depart Civil & Env Engn Durham NC 27780 USA Univ Michigan Dept Civil & Environm Engn 2160 G G Brown Bldg Ann Arbor MI 48109 USA

Knowledge of the structural response of bridges is extremely important for highway asset management and bridge structural health monitoring. Instrumenting every bridge in a road network and maintaining the monitoring instrumentation over decades of service can be financially infeasible. Mechanical intuition suggests a significant relationship exists between responses of two sets of bridges reasonably similar in design exposed to an identical load. This study explores the use of data-driven models to forecast the response of one bridge to a given truck load using the response of another bridge to the same loading profile. By deploying a modern monitoring system in multiple bridges in the same highway corridor integrated in a cyber-physical systems (CPS) framework, and utilizing advanced computer vision algorithms, the authors have gathered a unique dataset consisting of pairs of bridge responses to the same truck load from live traffic moving across a 32.2 km (20 miles) stretch of the I-275 highway in southeast Michigan. Signal processing techniques have been employed to isolate the response of the bridges to a single truck load in a time series of recorded responses. Then, a deep-learning-based time series forecasting framework using the encoder-decoder architecture with gated recurrent unit (GRU) and long short-term memory (LSTM) cells has been used for bridge response forecasting. Baseline models based on linear time series models are also developed to which the deep-learning forecasting models can be compared. After training the models, it is observed that deep-learning-based models can accurately forecast the response of one bridge using the response of another and reduce the forecasting root-mean-squared error (RMSE) by at least 20% relative to baseline linear models. The forecasting capabilities of the encoder-decoder architecture proposed herein outperform traditional approaches to response forecasting. Trained versions of the encoderdecoder forecasting model

关键词： Time Series Forecasting Bridge Monitoring encoder-decoder Recurrent Neural Networks Deep Learning Sequence-to-Sequence

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ICEAP: An advanced fine-grained image captioning network with enhanced attribute predictor

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DISPLAYS 2024年 84卷

作者： Hossen, Md. Bipul Ye, Zhongfu Abdussalam, Amr Hossain, Mohammad Alamgir Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China

Fine-grained image captioning is a focal point in the vision-to-language task and has attracted considerable attention for generating accurate and contextually relevant image captions. Effective attribute prediction and their utilization play a crucial role in enhancing image captioning performance. Despite progress in prior attribute-related methods, they either focus on predicting attributes related to the input image or concentrate on predicting linguistic context-related attributes at each time step in the language model. However, these approaches often overlook the importance of balancing visual and linguistic contexts, leading to ineffective exploitation of semantic information and a subsequent decline in performance. To address these issues, an Independent Attribute Predictor (IAP) is introduced to precisely predict attributes related to the input image by leveraging relationships between visual objects and attribute embeddings. Following this, an Enhanced Attribute Predictor (EAP) is proposed, initially predicting linguistic context-related attributes and then using prior probabilities from the IAP module to rebalance image and linguistic context-related attributes, thereby generating more robust and enhanced attribute probabilities. These refined attributes are then integrated into the language LSTM layer to ensure accurate word prediction at each time step. The integration of the IAP and EAP modules in our proposed image captioning with the enhanced attribute predictor (ICEAP) model effectively incorporates high-level semantic details, enhancing overall model performance. The ICEAP outperforms contemporary models, yielding significant average improvements of 10.62% in CIDEr-D scores for MS-COCO, 9.63% for Flickr30K and 7.74% for Flickr8K datasets using cross-entropy optimization, with qualitative analysis confirming its ability to generate fine-grained captions.

关键词： Fine-grained image caption Attention mechanism encoder-decoder Independent attribute predictor Enhanced attribute predictor

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LA-ResUNet: An Efficient Linear Attention Mechanism in ResUNet for the Semantic Segmentation of Pulmonary Nodules

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IEEE ACCESS 2024年 12卷 182894-182907页

作者： Sarah Prithvika, P. C. Jani Anbarasi, L. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

Numerous people die from lung cancer every year, making it a serious public health issue. Oftentimes, the symptoms of lung cancer manifest only at a later stage, when it is difficult to treat. Pulmonary nodules are commonly found while screening the lungs using a Computed Tomography (CT) scan, and some of the nodules may be cancerous. So, an efficient automated pulmonary nodule segmentation system is needed to isolate the pulmonary nodules from the scan images. The doctors can track the nodules that are likely to be malignant and provide early treatment if they become cancerous, thereby improving the patient's chance of survival. The attention mechanism is a technique that is often used in computer vision to enhance the neural network's performance. LA-ResUNet, a pulmonary nodule segmentation model, built using ResUNet with a linear attention mechanism and the Leaky ReLU activation function is proposed. LA-ResUNet efficiently segments pulmonary nodules, while achieving a linear time and space complexity. By employing residual blocks, it is possible to construct a deep network without facing the vanishing gradient problem. Additionally, it makes deep network training simpler. Skip connections allow for better gradient flow during training and better information flow between layers. Leaky ReLU addresses the dying ReLU scenario, a situation where some neurons cease to learn when the network is being trained. LA-ResUNet was used on the dataset LIDC-IDRI (The Lung Image Database Consortium and Image Database Resource Initiative) and it produced a dice score coefficient (DSC) of 73.11% and Intersection over Union score (IoU) of 60.62%.

关键词： Lungs Lung cancer Image segmentation Tumors Solids Glass Feature extraction Computed tomography Biomedical imaging Blood vessels Convolutional neural network encoder-decoder linear attention pulmonary nodule segmentation ResUNet

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Enhancing building segmentation by deep multiview classification for advancing sustainable urban development

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JOURNAL OF BUILDING ENGINEERING 2024年 83卷

作者： El Hajjar, Sally Kassem, Hassan Abdallah, Fahed Omrani, Hichem Luxembourg Inst Socio Econ Res LISER Urban Dev & Mobil Dept 11 Porte Sci L-4366 Esch Sur Alzette Luxembourg Lebanese Univ Beirut Lebanon Univ Lorraine Lab LCOMS Metz France

Accurate building segmentation plays a crucial role in a wide range of applications such as urban planning, monitoring, and mapping. Different deep learning models were employed for building segmentation. However, these models analyze images from a single view. Given the limitations of single-view building segmentation models, our research aims to enhance accuracy by proposing a novel multi-view U-Net deep model for accurate building segmentation that incorporates multiple views of the images. We employ two pre-trained convolutional neural network architectures, MobileNetV2 and ResNet50, to extract features representing two different views of our images. By fusing these features, our proposed method effectively captures complementary information, leading to enhanced segmentation accuracy. To further improve the model's performance, we incorporate skip connections and up-convolutional layers to ensure fine-grained feature propagation. Our experimental results on a large building dataset demonstrate a significant improvement in segmentation accuracy 91% compared to state-of-theart methods, highlighting the effectiveness of our multiview fusion approach. The experimental results enhance the benefits of creating different views by adopting the novel concept proposed in this paper. This research has the potential to redefine the landscape of building segmentation in applications such as urban planning and mapping. We also conducted a test on a large study area (city scale of Belval-Luxembourg). This demonstrates the capabilities of our method and its efficiency in segmenting satellite images from a large extent area and reinforces its potential for real-world applications.

关键词： Deep multiview building segmentation U-Net model Pix2pix Skip connection encoder-decoder

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Enhanced transformer model for video caption generation

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EXPERT SYSTEMS 2023年第0期

作者： Varma, Soumya Peter, J. Dinesh Karunya Inst Technol & Sci Dept CSE Coimbatore Tamil Nadu India

Automatic Video captioning system is a method of describing the content in a video by analysing its visual aspects with regard to space and time and producing a meaningful caption that explains the video. A decade of research in this area has resulted in a steep growth in the quality and appropriateness of the generated caption compared with the expected result. The research has been driven from the very basic method to most advanced transformer method. Machine generated caption of a video must be adhering to many expected standards. For humans, this task may be a trivial one, however its not the same for a machine to analyse the content and generate a semantically coherent description for it. The caption which is generated in a natural language must also adhere to its lexical and syntactical structure. The video captioning process is a culmination of computer vision and natural language processing tasks. Commencing with template based conventional approach, it has surpassed statistical method, traditional deep learning approaches and is now in the trend of using transformers. This work made an extensive study of the literature and has proposed an improved transformer-based architecture for video captioning process. The transformer architecture made use of an encoder and decoder model that has two and three sublayers respectively. Multi-head self attention and cross attention are part of the model which bring about very beneficial results. The decoder is auto-regressive and uses a masked layer to prevent the model from foreseeing future words in the caption. An enhanced encoder-decoder Transformer model with CNN for feature extraction has been used in our work. This model captures the long-range dependencies and temporal relationships more effectively. The model has been evaluated with benchmark datasets and compared with state-of-the-art methods and found to be slightly better in the performance. The performance scores are slightly varying for BLEU, METEOR, ROUGE a

关键词： curriculum learning deep learning encoder-decoder engineering applications neural networks transformer video caption

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Multi-Source Interactive Stair Attention for Remote Sensing Image Captioning

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REMOTE SENSING 2023年第3期15卷 579-579页

作者： Zhang, Xiangrong Li, Yunpeng Wang, Xin Liu, Feixiang Wu, Zhaoji Cheng, Xina Jiao, Licheng Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

The aim of remote sensing image captioning (RSIC) is to describe a given remote sensing image (RSI) using coherent sentences. Most existing attention-based methods model the coherence through an LSTM-based decoder, which dynamically infers a word vector from preceding sentences. However, these methods are indirectly guided through the confusion of attentive regions, as (1) the weighted average in the attention mechanism distracts the word vector from capturing pertinent visual regions and (2) there are few constraints or rewards for learning long-range transitions. In this paper, we propose a multi-source interactive stair attention mechanism that separately models the semantics of preceding sentences and visual regions of interest. Specifically, the multi-source interaction takes previous semantic vectors as queries and applies an attention mechanism on regional features to acquire the next word vector, which reduces immediate hesitation by considering linguistics. The stair attention divides the attentive weights into three levels-that is, the core region, the surrounding region, and other regions-and all regions in the search scope are focused on differently. Then, a CIDEr-based reward reinforcement learning is devised, in order to enhance the quality of the generated sentences. Comprehensive experiments on widely used benchmarks (i.e., the Sydney-Captions, UCM-Captions, and RSICD data sets) demonstrate the superiority of the proposed model over state-of-the-art models, in terms of its coherence, while maintaining high accuracy.

关键词： remote sensing image captioning cross-modal interaction attention mechanism semantic information encoder-decoder

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Polarization image fusion with self-learned fusion strategy

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PATTERN RECOGNITION 2021年 118卷 108045-108045页

作者： Zhang, Junchao Shao, Jianbo Chen, Jianlai Yang, Degui Liang, Buge Cent South Univ Sch Aeronaut & Astronaut Changsha 410083 Peoples R China

Polarization image fusion aims to integrate intensity and degree of linear polarization images into one with more details, which is beneficial to improve the ability of targets detection under complex background. The fusion strategies in conventional methods are designed in a hand-crafted way and not robust to different fusion tasks. In this paper, we propose a novel and deep network to address the polarization image fusion issue with self-learned strategy. The network consists of encoder, Fusion, and decoder layers. Feature maps extracted by encoder are fused, then fed into decoder to generate fused images. Besides, a novel loss function is adopted to train the network in an unsupervised way, without ground truth of fused images. To verify the advantage, the network trained on polarization images is also used to infrared and visible images fusion, and multi-focus image fusion. Experimental results showed that our method outperforms several state-of-the-art methods in terms of visual quality and quantitative measurement. The proposed fused method can be applied in the military and civilian fields such as camouflage and hidden targets detection, medical diagnosis, and environmental monitoring. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Image fusion encoder-decoder Polarization image Unsupervised learning

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Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning

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SENSORS 2021年第9期21卷 3152-3152页

作者： Liang, Peng Shi, Wenzhong Ding, Yixing Liu, Zhiqiang Shang, Haolv Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Piesat Informat Technol Co Ltd Beijing 100195 Peoples R China

Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning.

关键词： road extraction vector field learning high resolution remote sensing image encoder-decoder DCNN

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Hybrid Attention Distribution and Factorized Embedding Matrix in Image Captioning

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IEEE ACCESS 2020年 8卷 154453-154460页

作者： Wang, Jian Feng, Jie Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China

Self-attention mechanisms are widely used in current encoder-decoder frameworks of image-captioning tasks. These mechanisms use the transformer module as the basic unit to compute the similarity between query and key vectors, and normalize them to obtain the attention distribution matrix, weighting and reconstructing the rich semantic query vectors. However, a single attention distribution matrix cannot express more complex intrinsic relations between query and key vectors. Some very important intrinsic relations can thus become lost. We therefore propose a hybrid attention distribution (HAD) that allows multiple distributions to be reconstructed to express deeper internal relations and avoids a single shallow attention distribution. In addition, to reduce the number of model parameters, we factorize the word-embedding matrix. This effectively increases the training efficiency and prevents the metrics from decreasing. We extensively evaluate our proposals using the MS-COCO image-captioning dataset. Our results outperform existing state-of-the-art methods on some metrics.

关键词： Self-attention image captioning transformer encoder-decoder

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