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Upsampling Matters for Road Marking Segmentation of Autonomous Driving

IFAC-PapersOnLine 2020年第5期53卷 232-237页

作者： Ye Liu Xi Zhang Lei Liu Lei Zhang School of Mechanical Engineering Shanghai Jiao Tong University CSSC

Although autonomous driving have become applicable to the industry, the prevalent application of key techniques to the autonomous vehicles still needs to be refined. For instance, how to fast and accurately segment road markings in order to assist the next pedestrian path prediction and the creation of high-definition (HD) map respectively is useful for autonomous driving to be more practical. Current road marking segmentation mainly rely on the techniques of semantic segmentation of computer vision with encoder-decoder architecture. However, as demonstrated in this paper, the upsampling layer of convolutional neural networks with encoder-decoder architecture plays a significant role in the efficiency and accuracy of the road marking segmentation. The bilinear upsampling layer is fast due to its intrinsic simple interpolation but with less accuracy; on the contrary, the upsampling layer with offsets is relatively accurate but with more computational cost. Therefore, at least, in terms of prevalent application, efficiency, and accuracy, the upsampling layer of decoder of convolution neural networks should be paid more attention to for the next research work of autonomous driving.

关键词： Semantic Segmentation Upsampling encoder-decoder architecture Road Marking Autonomous Driving

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Deep Learning With Noisy Labels for Spatiotemporal Drought Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Cortes-Andres, Jordi Fernandez-Torres, Miguel-Angel Camps-Valls, Gustau Univ Valencia UV Image Proc Lab IPL Valencia 46980 Paterna Spain

Droughts pose significant challenges for accurate monitoring due to their complex spatiotemporal characteristics. Data-driven machine learning (ML) models have shown promise in detecting extreme events when enough well-annotated data is available. However, droughts do not have a unique and precise definition, which leads to noise in human-annotated events and presents an imperfect learning scenario for deep learning models. This article introduces a 3-D convolutional neural network (CNN) designed to address the complex task of drought detection, considering spatiotemporal dependencies and learning with noisy and inaccurate labels. Motivated by the shortcomings of traditional drought indices, we leverage supervised learning with labeled events from multiple sources, capturing the shared conceptual space among diverse definitions of drought. In addition, we employ several strategies to mitigate the negative effect of noisy labels (NLs) during training, including a novel label correction (LC) method that relies on model outputs, enhancing the robustness and performance of the detection model. Our model significantly outperforms state-of-the-art drought indices when detecting events in Europe between 2003 and 2015, achieving an AUROC of 72.28%, an AUPRC of 7.67%, and an ECE of 16.20%. When applying the proposed LC method, these performances improve by +5%, +15%, and +59%, respectively. Both the proposed model and the robust learning methodology aim to advance drought detection by providing a comprehensive solution to label noise and conceptual variability.

关键词： Droughts Noise measurement Training Data models Three-dimensional displays Noise Biological system modeling Solid modeling Predictive models Europe Convolutional neural networks (CNNs) drought detection encoder-decoder architecture hydro-climatological data label correction (LC) noisy labels (NLs) spatiotemporal data

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Multi-scale feature fusion network for pixel-level pavement distress detection

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AUTOMATION IN CONSTRUCTION 2022年第0期141卷

作者： Zhong, Jingtao Zhu, Junqing Huyan, Ju Ma, Tao Zhang, Weiguang Southeast Univ Sch Transportat Nanjing 211189 Peoples R China

Automatic pavement distress detection is essential to monitoring and maintaining pavement condition. Currently, many deep learning-based methods have been utilized in pavement distress detection. However, distress segmentation remains as a challenge under complex pavement conditions. In this paper, a novel deep neural network architecture, W-segnet, based on multi-scale feature fusions, is proposed for pixel-wise distress segmentation. The proposed W-segnet concatenates distress location information with distress classification features in two symmetric encoder-decoder structures. Three major types of distresses: crack, pothole, and patch are segmented and the results were discussed. Experimental results show that the proposed W-segnet is robust in various scenarios, achieving a mean pixel accuracy (MPA) of 87.52% and a mean intersection over union (MIoU) of 75.88%. The results demonstrate that W-segnet outperforms other state-of-the-art semantic segmentation models of U-net, SegNet, and PSPNet. Comparison of cost of model training and inference indicates that W-segnet has the largest number of parameters, which needs a slightly longer training time while it does not increase the inference cost. Four public datasets were used to test the generalization ability of the proposed model and the results demonstrate that the W-segnet possesses well segmentation performance.

关键词： Deep learning encoder-decoder architecture Pavement distress Feature fusion Semantic segmentation Unmanned aerial vehicle (UAV)

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Unidirectional RGB-T salient object detection with intertwined driving of encoding and fusion

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期114卷

作者： Wang, Jie Song, Kechen Bao, Yanqi Yan, Yunhui Han, Yahong Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

The U-shaped encoder-decoder architecture based on CNNs has been rooted in salient object detection (SOD) tasks, and it have revealed two drawbacks while driving the rapid development of saliency detection. (1) The inherent characteristics of CNNs dictate that it is difficult to learn long-range dependencies and model global correlations. (2) For the common purpose of improving the performance of saliency detection, the encoder and decoder should complement each other and work together. However, the existing encoder-decoder architecture treats encoder and decoder independently of each other. Specifically, the encoder is responsible for extracting features and the decoder fuses multi-level or multi-modal features to produce prediction maps. That is, the encoder alone needs to be responsible for the decoder, while the valuable information after the decoder fusion will not facilitate feature extraction. Therefore, we propose a unidirectional RGB-T salient object detection network with intertwined driving of encoding and fusion to solve the above problems. Firstly, we introduce transformer (SegFormer) as the backbone of the network to deal with the problem that CNNs are difficult to establish long-range dependence. Secondly, we constructed a unidirectional architecture where encoding and fusion are intertwined and mutually driving, which discards the drawbacks of encoder-decoder architecture to make the network more powerful and concise. Based on the unidirectional architecture, the proposed Local Detail-driven Fusion Module (LDFM) uses the fused features of the previous level to drive the cross-modal fusion at the current level. Meanwhile, the proposed Local Detail-driven Weighting Module (LDWM) uses the fused features to drive the cross-modal weighting. They will drive more effective features to be fed into the next level of the encoding block. Comprehensive experiments have verified the superior performance of our method on the RGB-T saliency detection task.

关键词： Salient object detection RGB-T Transformer encoder-decoder architecture

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Electrical lithium-ion battery models based on recurrent neural networks: A holistic approach

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JOURNAL OF ENERGY STORAGE 2023年 58卷

作者： Schmitt, Jakob Horstkoetter, Ivo Baeker, Bernard Inst Automot Mechatron IAM GmbH Freiberger Str 37 D-01067 Dresden Germany Univ Technol Dresden Inst Automot Engn Chair Vehicle Mechatron D-01067 Dresden Germany

As an efficient energy storage technology, lithium-ion batteries play a key role in the ongoing electrification of the mobility sector. However, the required model-based design process, including hardware in the loop solutions, demands precise battery models. In this work, an encoder-decoder model framework based on recurrent neural networks is developed and trained directly on unstructured battery data to replace time consuming characterisation tests and thus simplify the modelling process. A manifold pseudo-random bit stream dataset is used for model training and validation. A mean percentage error (MAPE) of 0.30% for the test dataset attests the proposed encoder-decoder model excellent generalisation capabilities. Instead of the recursive one-step prediction prevalent in the literature, the stage-wise trained encoder-decoder framework can instantaneously predict the battery voltage response for 2000 time steps and proves to be 120 times more time-efficient on the test dataset. Accuracy, generalisation capability and time efficiency of the developed battery model enable a potential online anomaly detection, power or range prediction. The fact that, apart from the initial voltage level, the battery model only relies on the current load as input and thus requires no estimated variables such as the state-of-charge (SOC) to predict the voltage response holds the potential of a battery ageing independent LIB modelling based on raw BMS signals. The intrinsically ageing-independent battery model is thus suitable to be used as a digital battery twin in virtual experiments to estimate the unknown battery SOH on purely BMS data basis.

关键词： Lithium-ion battery (LIB) Data-based battery model Recurrent neural network (RNN) Gated recurrent unit (GRU) encoder-decoder architecture

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Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

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SENSORS 2020年第16期20卷 4403-4403页

作者： Billah, Umme Hafsa La, Hung Manh Tavakkoli, Alireza Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we present a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation, while addressing the effect of gradient vanishing problem. A feature silencing module is incorporated in the proposed framework, capable of eliminating non-discriminative feature maps from the network to improve performance. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network's robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than the state-of-the-art crack detection architectures.

关键词： convolutional neural network encoder-decoder architecture semantic segmentation feature silencing crack detection

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CGFTNet: Content-Guided Frequency Domain Transform Network for Face Super-Resolution

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INFORMATION 2024年第12期15卷 765-765页

作者： Yekeben, Yeerlan Cheng, Shuli Du, Anyu Xinjiang Univ Sch Comp Sci & Technol Urumqi 830046 Peoples R China

Recent advancements in face super resolution (FSR) have been propelled by deep learning techniques using convolutional neural networks (CNN). However, existing methods still struggle with effectively capturing global facial structure information, leading to reduced fidelity in reconstructed images, and often require additional manual data annotation. To overcome these challenges, we introduce a content-guided frequency domain transform network (CGFTNet) for face super-resolution tasks. The network features a channel attention-linked encoder-decoder architecture with two key components: the Frequency Domain and Reparameterized Focus Convolution Feature Enhancement module (FDRFEM) and the Content-Guided Channel Attention Fusion (CGCAF) module. FDRFEM enhances feature representation through transformation domain techniques and reparameterized focus convolution (RefConv), capturing detailed facial features and improving image quality. CGCAF dynamically adjusts feature fusion based on image content, enhancing detail restoration. Extensive evaluations across multiple datasets demonstrate that the proposed CGFTNet consistently outperforms other state-of-the-art methods.

关键词： face super-resolution encoder-decoder architecture feature fusion channel attention visual transformer

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Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network

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SENSORS 2020年第23期20卷 6735-6735页

作者： Zhang, Yi Zhang, Shizhou Li, Ying Zhang, Yanning Northwestern Polytech Univ Shaanxi Prov Key Lab Speech & Image Informat Proc Natl Engn Lab Integrated Aerosp Ground Ocean Big Sch Comp Sci Xian 710129 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China

Timely and accurate change detection on satellite images by using computer vision techniques has been attracting lots of research efforts in recent years. Existing approaches based on deep learning frameworks have achieved good performance for the task of change detection on satellite images. However, under the scenario of disjoint changed areas in various shapes on land surface, existing methods still have shortcomings in detecting all changed areas correctly and representing the changed areas boundary. To deal with these problems, we design a coarse-to-fine detection framework via a boundary-aware attentive network with a hybrid loss to detect the change in high resolution satellite images. Specifically, we first perform an attention guided encoder-decoder subnet to obtain the coarse change map of the bi-temporal image pairs, and then apply residual learning to obtain the refined change map. We also propose a hybrid loss to provide the supervision from pixel, patch, and map levels. Comprehensive experiments are conducted on two benchmark datasets: LEBEDEV and SZTAKI to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.

关键词： change detection deep learning attentive coarse-to-fine encoder-decoder architecture end-to-end

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A spatiotemporal bidirectional network for video salient object detection using multiscale transfer learning

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INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2024年第2期13卷 25-25页

作者： Sharma, Gaurav Singh, Maheep NIT Uttarakhand Dept Comp Sci & Engn Srinagar India

Video saliency prediction aims to simulate human visual attention by selecting the most pertinent and important components within a video frame or sequence. When evaluating video saliency, time and space data are essential, particularly in the presence of challenging features such as fast motion, shifting background, and nonrigid deformation. The current video saliency frameworks are highly prone to failure under the specified conditions. Moreover, it is unsuitable to perform video saliency identification by solely relying on image saliency models, disregarding the temporal information in videos. This research proposes a novel Spatiotemporal Bidirectional Network for Video Salient Object Detection using Multiscale Transfer Learning (SBMTL-Net) to solve the issue of detecting important objects in videos. The SBMTL-Net produces significant outcomes for a given sequence of frames by utilizing Multi-scale transfer learning with an encoder and decoder technique to acquire knowledge and spatially and temporally map properties. SBMTL-Net model consists of bidirectional LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), where the VGG16 (Video Geometry Group) and VGG19 are utilized for multi-scale feature extraction of the input video frames. The performance of the proposed model has been evaluated on five publically available challenging datasets DAVIS-T, SegTrack-V2, ViSal, VOS-T and DAVSOD-T for the parameters MAE, F-measure and S-measure. The experimental results show the effectiveness of the proposed model as compared with other competitive models.

关键词： Video Saliency Spatiotemporal encoder-decoder architecture

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Automated hair removal in dermoscopy images using shallow and deep learning neural architectures

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Computer Methods and Programs in Biomedicine Update 2023年 4卷

作者： Delibasis, Konstantinos Moutselos, Konstantinos Vorgiazidou, Eleftheria Maglogiannis, Ilias Dept of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Dept. of Digital Systems University of Piraeus 80 Karaoli & Dimitriou Str Piraeus 18534 Greece

Removing hair from digital dermoscopy images is occasionally a necessary step before further analysis is applied to the images. This work considers two machine learning approaches that segment the hair pixels from dermoscopy images. Subsequently, morphological post-processing is applied to refine the segmented hair and an image inpainting algorithm replaces the hair pixels with values based on the surrounding image structures. The first hair segmentation approach combines pixel-wise features extracted using the well-known Gaussian image pyramid with a traditional shallow multilayer perceptron (MLP-ANN), to detect hair pixels in images. The second approach uses a deep neural convolutional encoder – decoder (ED) network to segment hair. Both hair segmentation methods (MLP-ANN and ED) are trained with a set of 32 dermoscopy images with manually annotated hair, whereas the MLP-ANN dataset is constructed in a pixel-wise manner. Both proposed methods underwent three different assessments. First a set of 50 images with a-priori known hair is used for hair segmentation evaluation. Secondly, a set of 13 different dermoscopy images with hair added using a suitably trained Generative Adversarial Network -GAN- are used to assess the quality of hair removal that generates the hair-free image, in terms of several error metrics with respect to the original hair-free image. Finally, both proposed hair segmentation methods (MLP-ANN and ED) are applied on a set of 200 hair and hair-free images, which is used for training an image classifier to recognize melanoma against nevi lesions and the improvement in the image classification accuracy is measured. Comparative results against several other state-of-the-art hair removal techniques are also presented. Results show that in terms of hair removal, both the proposed hair removal techniques outperform the best performing of the state-of-the-art methods under comparison, in terms of several error metrics. Considering the effect of hair re

关键词： Dermoscopy image encoder-decoder architecture Hair removal MLP-ANN

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