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ieee/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Gaus, Yona Falinie A. Bhowmik, Neelanjan Isaac-Medina, Brian K. S. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (纸本)9798350365474

The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot segmentation *** operates by generating masks based on various input prompts such as text, bounding boxes, points, or masks, introducing a novel methodology to overcome the constraints posed by dataset-specific scarcity. While SAM is trained on an extensive dataset, comprising more than 11M images, it mostly consists of natural photographic (visible band) images with only very limited images from other modalities. Whilst the rapid progress in visual infrared surveillance and X-ray security screening imaging technologies, driven forward by advances in deep learning, has significantly enhanced the ability to detect, classify and segment objects with high accuracy, it is not evident if the SAM zero-shot capabilities can be transferred to such modalities beyond the visible spectrum. For this reason, this work comprehensively assesses SAM capabilities in segmenting objects of interest in the X-ray and infrared imaging modalities. Our approach reuses and preserves the pre-trained SAM with three different prompts, namely bounding box, centroid and random points. We present several quantitative and qualitative results to showcase the performance of SAM on selected datasets. Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts. Specifically, SAM performs poorly in segmenting slender objects and organic materials, such as plastic bottles. Additionally, we find that infrared objects are also challenging to segment with point prompts given the low-contrast nature of this modality. Overall, this study shows that while SAM demonstrates outstanding zero-shot capabilities with box prompts, its performance ranges from moderate to poor for point prompts, indicating that special consideration on the cross-modal genera

关键词： Plastic bottles

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Multispectral Semantic Segmentation for Land Cover Classification: An Overview

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 14295-14336页

作者： Ramos, Leo Thomas Sappa, Angel D. Univ Autonoma Barcelona Comp Vis Ctr Barcelona 08193 Spain Kauel Inc Silicon Valley CA 94025 USA ESPOL Polytech Univ Guayaquil 090112 Ecuador

Land cover classification (LCC) is a process used to categorize the earth's surface into distinct land types. This classification is vital for environmental conservation, urban planning, agricultural management, and climate change research, providing essential data for sustainable decision making. The use of multispectral imaging (MSI), which captures data beyond the visible spectrum, has emerged as one of the most utilized image modalities for addressing this task. In addition, semantic segmentation techniques play a vital role in this domain, enabling the precise delineation and labeling of land cover classes within imagery. The integration of these three concepts has given rise to an intriguing and ever-evolving research field, witnessing continuous advancements aimed at enhancing multispectral semantic segmentation (MSSS) methods for LCC. Given the dynamic nature of this field, there is a need for a thorough examination of the latest trends and advancements to understand its evolving landscape. Therefore, this article presents a review of current aspects in the field of MSSS for LCC, addressing the following key points: 1) prevalent datasets and data acquisition methods;2) preprocessing methods for managing MSI data;3) typical metrics and evaluation criteria used for assessing performance of methods;4) current techniques and methodologies employed;and 5) spectral bands beyond the visible spectrum commonly utilized. Through this analysis, our objective is to provide valuable insights into the current state of MSSS for LCC, contributing to the ongoing development and understanding of this dynamic field while also providing perspectives for future research directions.

关键词： computer vision (CV) deep learning (DL) image segmentation land cover classification (LCC) multispectral imaging (MSI) semantic segmentation remote sensing satellite imagery deep learning (DL) image segmentation land cover classification (LCC) multispectral imaging (MSI) semantic segmentation remote sensing satellite imagery

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Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications, and Challenges

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ieee ACCESS 2025年 13卷 7366-7395页

作者： Gallagher, James E. Oughton, Edward J. George Mason Univ Geog & Geoinformat Sci Dept Fairfax VA 22030 USA

Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible light spectrum. This paper considers 400 total papers, reviewing 200 in detail to provide an authoritative meta-review of multispectral imaging technologies, deep learning models, and their applications, considering the evolution and adaptation of you only look once (YOLO). Ground-based collection is the most prevalent approach, totaling 63% of the papers reviewed, although uncrewed aerial systems (UAS) for YOLO-multispectral applications have doubled since 2020. The most prevalent sensor fusion is red-green-blue (RGB) with long-wave infrared (LWIR), comprising 39% of the literature. YOLOv5 remains the most used variant for adaption to multispectral applications, consisting of 33% of all modified YOLO models reviewed. Future research needs to focus on: 1) developing adaptive YOLO architectures capable of handling diverse spectral inputs that do not require extensive architectural modifications;2) exploring methods to generate large synthetic multispectral datasets;3) advancing multispectral YOLO transfer learning techniques to address dataset scarcity;and 4) innovating fusion research with other sensor types beyond RGB and LWIR.

关键词： YOLO Convolutional neural networks computer architecture Deep learning Reviews Accuracy Multispectral imaging Feature extraction computer vision Adaptation models Multispectral object detection you only look once (YOLO) convolutional neural networks (CNN) deep learning RGB LWIR HSI MSI NIR SAR

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Multi-sensor Ensemble-guided Attention Network for Aerial Vehicle Perception beyond visible spectrum

Multi-sensor Ensemble-guided Attention Network for Aerial Ve...

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ieee/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Kwasniewska, Alicja MacAllister, Anastacia Nicolas, Rey Garza, Javier SiMa Technol 226 Airport Pkwy San Jose CA 95110 USA Gen Atom 14200 Kirkham Way Poway CA 92064 USA Lockheed Martin 1 Lockheed Blvd Ft Worth TX 76108 USA

ISBN: (纸本)9798350302493

Researchers from different market domains have made significant developments in Artificial Intelligence (AI) enabling more advanced automated sensing systems and, thus, eliminating the need for the time-consuming manual analysis of data, which is prone to human errors. However, successful deployment of such systems in real world applications requires careful design and analysis of the proposed models. This work focuses on perception done on Unmanned Aerial Vehicles (UAV) using multi-task learning. There are multiple challenges when considering such platforms. First of all, they often operate in difficult and dynamic conditions affected by various factors, such as background noises, ego-noise of the motors and occluded views. At the same time, they require high performance local compute, co-designed with optimized software solutions that meet small size, weight, and power (SWaP) requirements. Therefore, the AI models designed for such systems should not introduce computational and memory overheads to allow for real time processing at the embedded edge. Taking this into account, this work proposes a novel neural network-based system that utilizes ensemble-guided modulations of audio path fused with the infrared (IR) visual embedding using the attention mechanism. The ensemble mechanism doesn't require spawning new ensemble members, but instead operates on FiLM (Feature-wise Linear Modulation) activation, making it suitable for resource-constraints embedded edge platforms. The performed experiments show that the proposed network outperforms a single FiLM network by 15% and is more robust to noise.

关键词： Real time systems

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Pseudo-label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection

Pseudo-label Generation and Various Data Augmentation for Se...

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ieee/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Yu, Ye Wang, Lei Ling, Qiang Univ Sci & Technol China Hefei Anhui Peoples R China Hefei ZhanDa Intelligence Technol Co Ltd Hefei Anhui Peoples R China Hefei Univ Technol Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning is a highly researched problem, but existing semi-supervised object detection frameworks are based on RGB images, and existing pre-trained models cannot be used for hyperspectral images. To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images. Next, in order to make full use of the unlabeled training set, we generate pseudo-labels with the model trained in the first stage and mix the obtained pseudo-labels with the labeled training set. Then, a large number of strong data augmentation methods are added to make the final model better. We achieve the SOTA, with an AP of 26.35, on the Semi-Supervised Hyperspectral Object Detection Challenge (SSHODC) in the CVPR 2022 Perception beyond the visible spectrum workshop, and win the first place in this Challenge.

关键词： Training computer vision Conferences Object detection Semisupervised learning Data models Pattern recognition

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Thermal Image Super-Resolution Challenge Results - PBVS 2022

Thermal Image Super-Resolution Challenge Results - PBVS 2022

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ieee/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Kim, Jin Kim, Dogun Li, Zhihao Jian, Yingchun Yan, Bo Cao, Leilei Qi, Fengliang Wang, Hongbin Wu, Rongyuan Sun, Lingchen Zhao, Yongqiang Li, Lin Wang, Kai Wang, Yicheng Zhang, Xuanming Wei, Huiyuan Lv, Chonghua Sun, Qigong Tian, Xiaolin Jia, Zhuang Hu, Jiakui Wang, Chenyang Zhong, Zhiwei Liu, Xianming Jiang, Junjun Escuela Super Politecn Litoral ESPOL Guayaquil Ecuador Comp Vision Ctr Campus UAB Bellaterra 08193 Spain

ISBN: (纸本)9781665487399

This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception beyond the visible spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year's challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community's interest in this hot topic.

关键词： Training Deep learning Conferences Superresolution Transformers Particle measurements Pattern recognition

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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ieee/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Rangnekar, Aneesh Mulhollan, Zachary Vodacek, Anthony Hoffman, Matthew Sappa, Angel Blasch, Erik Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Gao, Fang Yu, Ye Shuang, Feng Wang, Lei Ling, Qiang Shyam, Pranjay Yoon, Kuk-Jin Kim, Kyung-Soo Rochester Inst Technol Rochester NY 14623 USA ESPOL Polytech Univ Guayaquil Ecuador Comp Vision Ctr Campus UAB Barcelona Spain US Air Force Res Lab Rome NY USA

ISBN: (纸本)9781665487399

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception beyond the visible spectrum (PBVS) 2022 workshop at the computer vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 x 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. [GRAPHICS] .

关键词： Training computer vision Conferences Training data Object detection Semisupervised learning Transformers

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Digital and Physical-World Attacks on Remote Pulse Detection 22

Digital and Physical-World Attacks on Remote Pulse Detection

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22nd ieee/CVF Winter Conference on Applications of computer vision (WACV)

作者： Speth, Jeremy Vance, Nathan Flynn, Patrick Bowyer, Kevin W. Czajka, Adam Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9781665409155

Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor. We present the first examples of presentation attacks in the digital and physical domains on rPPG from face video. Digital attacks are easily performed by adding imperceptible periodic noise to the input videos. Physical attacks are performed with illumination from visible spectrum LEDs placed in close proximity to the face, while still being difficult to perceive with the human eye. We also show that our attacks extend beyond medical applications, since the method can effectively generate a strong periodic pulse on 3D-printed face masks, which presents difficulties for pulse-based face presentation attack detection (PAD). The paper concludes with ideas for using this work to improve robustness of rPPG methods and pulse-based face PAD.

关键词： Performance evaluation computer vision Lighting Medical services Photoplethysmography Light emitting diodes Robustness

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beyond the visible spectrum: Is Person Identity Well Preserved in Thermal Cameras? 1

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14th International Conference on Computational Collective Intelligence (ICCCI)

作者： Ben Said, Afef Fradi, Hajer Lamouchi, Dorra Marnissi, Mohamed Amine Univ Sousse Inst Super Sci Appl & Technol Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia Univ Sousse Ecole Natl Ingn Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia Univ Sfax Ecole Natl Ingn Sfax Sfax 3038 Tunisia

ISBN: (数字)9783031160141

ISBN: (纸本)9783031160141;9783031160134

Face recognition is a well investigated problem in the computer vision community. beyond the visible spectrum, it is identified as an active-oriented and attractive research area. In particular, thermal cameras have recently emerged as increasingly important sensors for visual surveillance applications. In addition to the fact that these cameras operate well in challenging environments such as adverse weather and lighting conditions, they are commonly known as keystone biometric solution that preserves person identity. In this paper, we intend to prove that faces could be highly recognized from thermal cameras using a powerful generative adversarial model. This model is employed to deal with the domain shift between thermal and visible sensors. Extensive experiments of different generative models and face recognition systems demonstrate the effectiveness of the proposed pipeline to reveal the person identity even though it is acquired by different sensing modalities, with significant facial variations.

关键词： Generative Adversarial Network Thermal visible Face recognition Cross-spectral

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception beyond the visible spectrum CVPR 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors Conferences Superresolution Benchmark testing Transformers

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