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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Research on Pothole Road Detection and recognition Based on PyTorch and Improved YOLOv5 7

Research on Pothole Road Detection and Recognition Based on ...

引用

7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Guihua Dai, Junchao Zeng, Ruyi Liu, Yin Lei, Bin Huang, Jiali Chengdu Jincheng College Department of Computer and Software Sichuan China

ISBN: (纸本)9798350350890

Potholes on road surfaces are one of the pressing issues in urban road maintenance. They not only damage the stability and efficiency of vehicles but also pose a potential threat to the safety of pedestrians and passengers. In view of the limitations of YOLOv5 in feature representation, adaptability to complex scenes, and utilization efficiency of computational resources, this study introduces the CBAM (Convolutional Block Attention Module) attention mechanism into its backbone network. By combining channel attention and spatial attention, the model is able to focus more accurately on the key regions in the image, thereby optimizing feature representation. The improved network model is implemented using the deep learning framework PyTorch, and the model is quantitatively evaluated using metrics such as mAP (mean Average Precision). Experimental results demonstrate that the proposed model exhibits excellent performance in terms of detection accuracy and efficiency, which is of significant importance for research and applications in fields such as geological exploration, space science, and natural disasters. © 2024 ieee.

关键词： Disasters

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Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning

Practical Cross-modal Manifold Alignment for Robotic Grounde...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Nguyen, Andre T. Richards, Luke E. Kebe, Gaoussou Youssouf Raff, Edward Darvish, Kasra Ferraro, Frank Matuszek, Cynthia Booz Allen Hamilton Mclean VA 22102 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9781665448994

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Pro-crustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.

关键词： Manifolds Measurement Learning systems computer vision conferences Natural languages Robot sensing systems

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Architectural Adversarial Robustness: The Case for Deep Pursuit

Architectural Adversarial Robustness: The Case for Deep Purs...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cazenavette, George Murdock, Calvin Lucey, Simon Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by refraining each layer of a feed forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deepen real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.

关键词： Deep learning Resistance computer vision Sensitivity computer architecture Sparse representation Robustness

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Contrastive Learning for Sports Video: Unsupervised Player Classification

Contrastive Learning for Sports Video: Unsupervised Player C...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Koshkina, Maria Pidaparthy, Hemanth Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9781665448994

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

关键词： Training Heating systems computer vision Image color analysis conferences Games Real-time systems

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Perceptual Image Quality Assessment with Transformers

Perceptual Image Quality Assessment with Transformers

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cheon, Manri Yoon, Sung-Jun Kang, Byungyeon Lee, Junwoo LG Elect Seoul South Korea

ISBN: (纸本)9781665448994

In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) backbone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models [18, 55], we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge [23]. Our work will be an opportunity to further expand the approach for the perceptual IQA task.

关键词： Image quality Measurement Image resolution Head Feature extraction Quality assessment pattern recognition

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The Role of AI in Vehicle-to-Infrastructure Communication for Tolling Systems

The Role of AI in Vehicle-to-Infrastructure Communication fo...

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2024 ieee International Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2024

作者： Mahalakshmi, S. Baskaran, Nithiya Partheeban, Pachaivanna Krishnara, Kowsalyadevi Arjun, S. Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur Chennai India Chennai Institute of Technology Department Cyber Security Kundrathur Chennai India Chennai Institute of Technology Department Civil EngDepartment Cyber Security Kundrathur Chennai India Sona Institute of Technology Department of Cyber Security Salem India

ISBN: (纸本)9798331535476

The number of vehicles people use is substantial;thus, surveillance must be accurate. License plates are detected and recognized by detecting static and real-time images captured by the camera using OCR technology, computer vision, and AI in the tool booth and other important roads. This model can be used for many things, such as automated services, increased security, determining whether a car is registered, and identifying and validating vehicles connected to traffic violations. Real-time investigations are part of the automated recognition of license plates and their application to other traffic control and highway surveillance aspects. To detect plates from the images and in real-time video, Use Easy OCR with the license plates to read the plate number, utilize tensor flow object detection to detect and recognize, and save the detected license plate for further analysis and search. © 2024 ieee.

关键词： Highway traffic control

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CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

CNN-based morphological decomposition of X-ray images for de...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Madmad, Tahani Delinte, Nicolas De Vleeschouwer, Christophe UCLouvain ICTEAM Louvain La Neuve Belgium

ISBN: (纸本)9781665448994

This paper introduces a new learning based framework for X-ray images that relies on a morphological decomposition of the signal into two main components, separating images into local textures and piecewise smooth (cartoon) parts. The piecewise smooth component corresponds to the spatial variation of the average density of the objects, whereas the local texture component presents the inspected objects singularities. Our method builds on two convolutional neural network (CNN) branches to decompose an input image into its two morphological components. This CNN is trained with synthetic data, generated by randomly picking piecewise smooth and singular patterns in a parametric dictionary and enforcing the sum of the CNN branches to approximate the identity mapping. We demonstrate the relevance of the decomposition by enhancing the local textures component compared to the piecewise smooth one. Those enhanced images compare favorably to the ones obtained with existing works destined to visualize High Dynamic Range (HDR) images such as tone-mapping algorithms.

关键词： computer vision Dictionaries Heuristic algorithms conferences Data visualization Dynamic range Approximation algorithms

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Single View Geocentric Pose in the Wild

Single View Geocentric Pose in the Wild

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Christie, Gordon Foster, Kevin Hagstrom, Shea Hager, Gregory D. Brown, Myron Z. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images;however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object's geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available(1).

关键词： Training Earth computer vision Satellites Laser radar conferences Semantics

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Generalizable Multi-Camera 3D Pedestrian Detection

Generalizable Multi-Camera 3D Pedestrian Detection

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lima, Joao Paulo Roberto, Rafael Figueiredo, Lucas Simoes, Francisco Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil

ISBN: (纸本)9781665448994

We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.

关键词： Training computer vision Three-dimensional displays Sensitivity Fuses conferences Pose estimation

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Learning a Facial Expression Embedding Disentangled from Identity

Learning a Facial Expression Embedding Disentangled from Ide...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Wei Ji, Xianpeng Chen, Keyu Ding, Yu Fan, Changjie Netease Fuxi AI Lab Virtual Human Grp Beijing Peoples R China Univ Sci & Technol China Langfang Peoples R China

ISBN: (纸本)9781665445092

The facial expression analysis requires a compact and identity-ignored expression representation. In this paper, we model the expression as the deviation from the identity by a subtraction operation, extracting a continuous and identity-invariant expression embedding. We propose a Deviation Learning Network (DLN) with a pseudo-siamese structure to extract the deviation feature vector. To reduce the optimization difficulty caused by additional fully connection layers, DLN directly provides high-order polynomial to nonlinearly project the high-dimensional feature to a low-dimensional manifold. Taking label noise into account, we add a crowd layer to DLN for robust embedding extraction. Also, to achieve a more compact representation, we use hierarchical annotation for data augmentation. We evaluate our facial expression embedding on the FEC validation set. The quantitative results prove that we achieve the state-of-the-art, both in terms of fine-grained and identity-invariant property. We further conduct extensive experiments to show that our expression embedding is of high quality for expression recognition, image retrieval, and face manipulation.

关键词： Manifolds computer vision Image recognition Head Annotations Face recognition Image retrieval

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