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Pothole Detection and Filling System using image processing and Machine Learning

Pothole Detection and Filling System using Image processing ...

引用

2024 international conference on Smart Systems for applications in Electrical Sciences, ICSSES 2024

作者： Mahalingesh, T.C. Anubhav Mishra, Harshit Arun, R.V. Anand, Anshuman Siddaganga Institute of Technology Dept. of E&CE Tumakuru India

ISBN: (纸本)9798350364040

Potholes pose a significant risk to vehicles and pedestrians, necessitating prompt action to be addressed. In this project, a strategy is incorporated for pothole detection, utilizing the YOLOv8 object detection algorithm and trained on datasets collected from metropolitan roads exhibiting potholes. The transfer-learning is used to train the respective model, achieving an average precision of 0.82 and a recall of 0.79 upon testing with a dedicated dataset. Furthermore, the model is integrated with hardware components such as Raspberry Pi and Arduino Mega. The Raspberry Pi, equipped with a PiCam serves as a processing unit for real-time analysis of road conditions, while the Arduino Mega facilitates control over the vehicle via serial communication. This integration not only improves speed and accuracy but also reduces human labor, maintenance costs and enhances road safety. © 2024 ieee.

关键词： Object detection

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Remote Estimation of Peripheral Oxygen Saturation and Pulse Rate From Facial Analysis Using a Smartphone Camera

Remote Estimation of Peripheral Oxygen Saturation and Pulse ...

引用

45th Annual international conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Petersen, Andreas Gronbech Kjaer, Magnus Ruud Sorensen, Helge B. D. Tech Univ Denmark Dept Hlth Technol Biomed Signal Proc & AI Res Grp DK-2800 Lyngby Denmark

ISBN: (纸本)9798350324471

Vital sign monitoring is an invaluable tool for healthcare professionals, both in the hospital and at home. Traditional measurement devices provide accurate readings but require physical contact with the patient which often is unsuitable, furthermore contact-based devices have been reported to fail by loosing contact due to movement as severe events occur, therefore, a contactless method is necessary. We hypothesize that, in ideal scenarios, it is possible to estimate both SpO(2) and pulse rate using only facial video recorded with a smartphone's front-facing camera. To test this hypothesis, a dataset of 10 healthy subjects performing various breathing patterns while being recorded with a smartphone camera was collected during ideal lighting conditions. Using advanced image and signal processing methods to acquire remote photoplethysmography (rPPG) estimates from a patient's forehead, our proposed method can achieve SpO(2) estimation results with A(rms) = 1.34% (accuracy RMS) and MAE +/- STD = 1.26 +/- 0.68% (mean average error) across a SpO(2) range of 92% to 99% (percentage point SpO(2)) and pulse rate estimation results with A(rms) = 3.91 bpm (beats per minute) and MAE +/- STD = 3.24 +/- 2.11 bpm across a pulse rate range of 60 bpm to 90 bpm. We conclude from these results, that remote vital sign estimation using facial videos recorded entirely with a smartphone camera is possible.

关键词： Smartphones

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Design and Implementation of image signal Processor Based on CUDA 7

Design and Implementation of Image Signal Processor Based on...

引用

7th international conference on signal and image processing, ICSIP 2022

作者： Zhang, Bo Xu, Tu Chang, Yuchun Li, Zhen Li, Yukun Dalian University of Technology School of Microelectronics Dalian China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

With the rapid development of image signal processor (ISP), the implementation of ISP based on CPU or FPGA has become a major focus of current research. However, the implementation of CPU-based ISP has a long runtime and the implementation of FPGA-based ISP is inflexible, difficult to program and debug, and unable to display image in real time. To solve these problems, this paper designs and implements an ISP based on CUDA with the advantages of short runtime, easy debugging, and real-time image display. The designed ISP includes pipeline design, vivid color manipulation, CPU and GPU co-processing, and the selection of output and display image at any stage. The experimental results show that for images with resolution of 2000 x 3008, the speedup ratio can reach 63 without considering the data transfer time and more than 53 with considering the data transfer time. © 2022 ieee.

关键词： Graphics processing unit

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Volumetric 3D Reconstruction with Window-Wise Global Feature Aggregation 48

Volumetric 3D Reconstruction with Window-Wise Global Feature...

引用

48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Ren, Shihao Ding, Yikang Liao, Jinli Li, Xinghui Guo, Jia Feng, Wensen Wang, Xueqian Tsinghua University China

ISBN: (纸本)9781728163277

Volumetric 3D reconstruction methods have shown great performance in reconstructing indoor scenarios from monocular videos. However, as such approaches utilize discrete feature voxels to encode the observed scenes, the global feature interaction within and across different voxels is ignored, leading to imperfect reconstructions. To solve this problem, we propose a novel volumetric 3D reconstruction method named VolGARecon. The core portion of VolGARecon includes two parts: first, we use an MLP-based weighted fusion module (WFM) to unproject the extracted features to each voxel, which considers the visibility and is capable to reduce the noise caused by occlusion;second, a 3D transformer module (3DTR) is used to perform window-wise global feature interaction in a local sliding window, which strengthens the feature expression in 3D space and benefits estimating more complete and spatially coherent 3D models. In addition, we propose a multi-dimensional hybrid loss (MHL) that incorporates the 3D supervision in classical volumetric methods and the 2D supervision in novel view synthesis works. Extensive experiments show our method achieves superior performance on multiple datasets. © 2023 ieee.

关键词： image reconstruction

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Identification of Skin Conditions with Convolutional Neural Networks: A Deep Learning Approach 3

Identification of Skin Conditions with Convolutional Neural ...

引用

3rd international conference on Digital Transformation and applications, ICDXA 2024

作者： Leong, Jing En Goh, Ching Pang Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

This paper presents a robust approach for detecting skin diseases using advanced image detection methods supported by Convolutional Neural Network (CNN) models. Traditionally, skin diseases are diagnosed by experienced dermatologists. However, due to a scarcity of these specialists, there is a pressing need for alternative, reliable solutions. Our methodology capitalizes on image detection techniques to accurately and efficiently identify potential skin diseases from photographs. The cornerstone of our approach is the CNN model, which is renowned for its efficacy in processing visual data. The system we developed demonstrates a high level of accuracy, achieving a validation accuracy of at least 80 % in the identification of specific skin diseases. This level of precision is particularly noteworthy, considering the inherent challenges in diagnosing skin conditions through images. Despite this success, our research also acknowledges the difficulties in gathering a vast, diverse dataset of skin disease images from the internet. Such datasets are crucial for training and refining our model to ensure its effectiveness across various skin types and conditions. Furthermore, this paper investigates the factors that might lead to inaccuracies in predictions. It addresses common issues such as variations in image quality, lighting conditions, and the presence of similar-looking skin conditions, which can all potentially confound the model's decision-making process. By understanding and mitigating these factors, we aim to enhance the model's reliability and accuracy, moving closer to bridging the gap between automated detection and expert human diagnosis. This research not only contributes to the field of medical image analysis but also offers a promising tool for assisting dermatologists and improving patient outcomes in skin disease diagnosis. © 2024 ieee.

关键词： image classification

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Perceptual Quality Assessment for Digital Human Heads 48

Perceptual Quality Assessment for Digital Human Heads

引用

48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Zhang, Zicheng Zhou, Yingjie Sun, Wei Min, Xiongkuo Wu, Yuzhe Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China Peng Cheng Laboratory China Donghua University College of Information Science & Technology China

ISBN: (纸本)9781728163277

Digital humans are attracting more and more research interest during the last decade, the generation, representation, rendering, and animation of which have been put into large amounts of effort. However, the quality assessment of digital humans has fallen behind. Therefore, to tackle the challenge of digital human quality assessment issues, we propose the first large-scale quality assessment database for three-dimensional (3D) scanned digital human heads (DHHs). The constructed database consists of 55 reference DHHs and 1,540 distorted DHHs along with the subjective perceptual ratings. Then, a simple yet effective full-reference (FR) projection-based method is proposed to evaluate the visual quality of DHHs. The pretrained Swin Transformer tiny is employed for hierarchical feature extraction and the multi-head attention module is utilized for feature fusion. The experimental results reveal that the proposed method exhibits state-of-the-art performance among the mainstream FR metrics. The database is released at https://***/zzc-1998/DHHQA. © 2023 ieee.

关键词： Digital human head full-reference projection-based quality assessment database

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IoT Enhanced Resilience: Advanced AI Technique for Leak Detection and Predictive Maintenance in Oil and Gas Pipeline 10

IoT Enhanced Resilience: Advanced AI Technique for Leak Dete...

引用

10th international conference on Communication and signal processing, ICCSP 2024

作者： Juliet, A.Hency Legapriyadharshini, N. Malathi, P. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Applications Chennai602105 India

ISBN: (纸本)9798350353068

The study seeks to investigate how the implementation of IoT-driven advanced AI methods can bolster the robustness of oil and gas pipelines by improving strategies for detecting leaks and conducting predictive maintenance. The implementation of IoT in oil and gas pipelines poses challenges, such as the need for strong cybersecurity measures to safeguard sensitive data, addressing complexities in integrating IoT devices and advanced AI technologies, and requiring seamless coordination among diverse components, potentially leading to difficulties in compatibility, interoperability, and overall system integration. Addressing the obstacles in deploying IoT for oil and gas pipelines involves utilizing advanced AI methods like federated learning to enhance data privacy and adopting a modular integration approach, offering solutions to ensure strong cybersecurity, smooth coordination, and enhanced compatibility and interoperability throughout the infrastructure. Overcoming challenges in implementing IoT for oil and gas pipelines includes the application of advanced AI techniques, such as federated learning for improved data privacy, and adopting a modular integration strategy, providing solutions to ensure robust cybersecurity, seamless coordination, and increased compatibility and interoperability across the infrastructure. © 2024 ieee.

关键词： Leak detection

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MFA-DAF: Unsupervised Multimodal Medical image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy 2

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

引用

2nd international conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Xie, Xinyu Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun University of South China School of Electrical Engineering Hengyang China University of South China School of Computing/Software Hengyang China

ISBN: (纸本)9798350331417

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality. © 2023 ieee.

关键词： Detail-aware Fine-grained information Fourier Transform Multimodal medical image fusion

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CU-NET: TOWARDS CONTINUOUS MULTI-CLASS CONTOUR DETECTION FOR RETINAL LAYER SEGMENTATION IN OCT imageS 29

CU-NET: TOWARDS CONTINUOUS MULTI-CLASS CONTOUR DETECTION FOR...

引用

ieee international conference on image processing (ICIP)

作者： Bhattarai, Ashuta Kambhamettu, Chandra Jin, Jing Univ Delaware Video Image Modeling & Synth VIMS Lab Newark DE 19716 USA Nemours Childrens Hosp Newark DE USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recent deep learning-based contour detection studies show high accuracy in single-class boundary detection problems. However, this performance does not translate well in a multi-class scenario where continuous contours are required. Our research presents CU-Net, a U-Net-based network with residual-net encoders which can produce accurate and uninterrupted contour lines for multiple classes. The critical factor behind this concept is our continuity module, containing an interpolation layer and a novel activation function that converts discrete signals into smooth contours. We find the application of our approach in medical imaging problems like retinal layer segmentation from optical coherence tomography (OCT) scans. We applied our method to an expert annotated OCT dataset of children with sickle-cell disease. To compare with benchmarks, we evaluated our network on DME and HC-MS datasets. We achieved an overall mean absolute distance of 6.48 +/- 2.04 mu M and 1.97 +/- 0.89 mu M, respectively 1.03 and 1.4 times less than the current state-of-the-art.

关键词： Contour detection optical coherence tomography retinal layer segmentation

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VESSEL SEGMENTATION AND DIRT/REFLECTION DETECTION FOR RETINAL FUNDUS PHOTOGRAPHS 29

VESSEL SEGMENTATION AND DIRT/REFLECTION DETECTION FOR RETINA...

引用

ieee international conference on image processing (ICIP)

作者： Chang, Meng-Hsuan Chen, Chih-Ying Yu, Chih-Han Shao, Hao-Chiang Lin, Chia-Wen Natl Tsing Hua Univ Inst Commun Engn Hsinchu Taiwan Fu Jen Catholic Univ Dept Stat & Informat Sci New Taipei Taiwan Natl Chung Hsing Univ Inst Data Sci & Informat Comp Taichung Taiwan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

We propose an adversarial training framework to simultaneously address the vessel segmentation and dirt/reflection removal problems in fundus photographs used for diabetic retinopathy diagnosis. This framework contains two primary subnetworks, each triggered by a set of loss terms, i.e., one for segmentation and the other for reconstruction. These two subnetworks act as inverse functions of each other so that they form an autoencoder framework with a 2-dimensional latent code, which can be a vessel segmentation mask after binarization. To further improve the segmentation and reconstruction performance, we devise a loss function based on gradient vector flow (GVF) and re-organize the generator network. Experimental results show that the proposed method has a good generalization capability. Trained on DRIVE's training set, our model can produce segmentation and reconstruction-based artifact removal results stably on other datasets like CHASE-DB1 and STARE. The average F1-score of our segmentation results of DRIVE's testing set reaches 0.7964, and the artifact-free reconstruction results can achieve an average PSNR of 24 dB.

关键词： diabetic retinopathy (DR) retinal fundus image segmentation reconstruction

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