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Low-Illumination Lane Detection by Fusion of Multi-light Information

Low-Illumination Lane Detection by Fusion of Multi-light Inf...

引用

IEEE International conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Zhao, Wenbo Tian, Wei Han, Yi Yu, Xianwang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Lane detection based on the visual sensor is of great significance for the environmental perception of the intelligent vehicle. Current mature lane detection algorithms are trained and implemented in good visual conditions. However, the low-light environment such as in the night is much more complex, easily causing misdetections and even perception failures, which are harmful to the downstream tasks such as behavior decision and control of ego-vehicle. To tackle this problem, we propose a new lane detection algorithm that introduces the multi-light information into lane detection task. The proposed algorithm adopts a multi-exposure image processing module, which generates and fuses multi-exposure information from the source image data. By integrating this module, mainstream lane detection models can jointly learn the extraction of lane features as well as the enhancement of low-exposed image, thus improving both the performance and robustness of lane detection in the night.

关键词： lane detection image enhancement multi-exposure fusion

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Integrating the dynamic optimization strategies of Adam and AdaGrad

Integrating the dynamic optimization strategies of Adam and ...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Zhixuan Huang Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates.

关键词： Training Heuristic algorithms Optimization methods Machine translation Logic Artificial intelligence Robots Recommender systems Convergence Intelligent control

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image processing-Based Presentation Control System Using Binary Logic Technique 1st

Image Processing-Based Presentation Control System Using Bin...

引用

1st International conference on Expert Clouds and Applications, ICOECA 2023

作者： Chinchmalatpure, Sheela Ingale, Harshal Jadhao, Rushikesh Ghule, Ojasvi Ingole, Madhura Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819917440

Providing a perfect presentation becomes a challenging job because of various factors like changing the slides and the correct keys to be used to change the slides while maintaining composure in front of the audience. There are a lot of interruptions during a presentation when using the keyboard for controlling the slides. Therefore, this paper proposes a smart presentation system using hand gestures which provides an easy way to change or operate the slides. The proposed system uses various algorithms to detect different hand gestures to perform several operations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Adaptive Rate Block Compressive Sensing Based on Algorithmic Reconstruction Error

Adaptive Rate Block Compressive Sensing Based on Algorithmic...

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Chonghuan Song Jianhua Chen School of Information Science and Engineering Yunnan University Kunming Yunnan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Compressive sensing (CS) for a full-sized image requires a large sensing matrix, consuming significant storage space and computational resources. Block-based CS addresses this but allocates the same number of samples to each block, ignoring the sparsity differences among various blocks. Additionally, the original image's sparsity is unknown in practical systems. Therefore, it is necessary to accurately estimate the sparsity from some initial sampling information. This paper proposes an adaptive rate block CS model based on the difference of the results of two reconstruction algorithms. The image is initially sampled at the sampling end. Then a high-accuracy algorithm and a low-accuracy algorithm are used to reconstruct initial sampled signals respectively. The sparsity of each image block is estimated by the difference between the two initial reconstructed signals, allowing the appropriate allocation of the number of samples for a block. The main computations of this scheme are concentrated at the reconstruction end, which can effectively save resources at the sampling end. Experimental results show that the proposed scheme can effectively improve reconstruction quality while reducing the sampling rate.

关键词： Surveillance Measurement uncertainty Signal processing algorithms Reconstruction algorithms Signal processing Sensors Resource management image reconstruction Compressed sensing Videos

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Classification and detection of skin diseases based on CNN-powered image segmentation 3

Classification and detection of skin diseases based on CNN-p...

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3rd IEEE International conference on Intelligent Technologies, CONIT 2023

作者： Vasudeva Reddy, T. Anirudh Reddy, R. Bhanu Teja, C.S. Sai Charan Reddy, N. Chandra Sekhar Rao, N. Hima B V Raju Institute of Technology Department of ECE Telangana India

ISBN: (纸本)9798350338607

Skin diseases have become more prevalent in recent years, posing a significant burden on healthcare systems globally. These conditions can range from noncancerous problems like acne, eczema, and vitiligo to more severe diseases such as melanoma, basal and squamous cell carcinoma. Skin ailments not only have an impact on physical health but can also affect the patient's psychological well-being, especially when the face is disfigured or damaged. Early detection and diagnosis of dermatological conditions can significantly improve survival rates as their severity can worsen over time. This article covers various methods used to identify and diagnose skin diseases, including natural diagnostic techniques and image pre-processing mechanisms and classification algorithms used globally. By understanding these techniques, healthcare professionals can detect skin diseases accurately, leading to better patient outcomes. Advancements in technology and research in this field continue to progress, providing hope to those who suffer from skin diseases. © 2023 IEEE.

关键词： Python

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Deep Learning-Based Depth Map Defect Removal for Industrial Applications 10

Deep Learning-Based Depth Map Defect Removal for Industrial ...

引用

conference on Optical Metrology and Inspection for Industrial Applications X

作者： Voronin, V. Gapon, N. Zhdanova, M. Tokareva, O. Khamidullin, I. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510667877;9781510667884

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.

关键词： depth map defect detection image reconstruction manufacturing deep learning U-Net

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Recognition and Analysis of Glass Subsurface Defects by Integrating Machine Vision and image processing Technology

Recognition and Analysis of Glass Subsurface Defects by Inte...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Chen Lin Xiong Ling Guangming Luo Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies.

关键词： Visualization image recognition Machine vision Metals Glass Optical imaging Classification algorithms Manufacturing Surface treatment Optical devices

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Design and Implementation of Railway Track Foreign Object image processing Platform Based on GUI

Design and Implementation of Railway Track Foreign Object Im...

引用

International conference on Computers, Communications, and systems (ICCCS)

作者： Xiqian LEI Tiantian XU Liqun BAO Ying LI Electronic Information Engineering College LanZhou Institute of Technology Lanzhou China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Railway plays a leading role in the field of transportation in China and shoulders the important mission of driving the development of national economy. In view of the changeable environment of railway track, the processing algorithm with good real-time performance, strong robustness and high accuracy in the process of foreign body detection is the key to achieve rapid detection. In order to facilitate researchers to compare and analyze the effects of relevant algorithms intuitively and quickly, the GUI visual interactive interface was used to design the simulation platform for track foreign object image processing, conceive the design process, build the overall design of the platform, and divide the static foreign object detection and moving target detection modules according to the realization functions. In the static foreign body detection module, the image of track foreign body is input to determine whether there is track foreign body and give early warning. Meanwhile, the processing effect of different algorithms can be visually compared through the operation results. In the moving target detection module, the moving target in the input video is marked and tracked. The test results of the simulation platform show that the processing platform is simple and easy to operate, and can effectively assist researchers to deepen the understanding and application of orbital foreign object image processing.

关键词： Visualization Target tracking image processing Transportation Object detection Rail transportation Robustness

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Underwater image enhancement and detection based on convolutional DCP and YOLOv5 41

Underwater image enhancement and detection based on convolut...

引用

第41届中国控制会议

作者： Liu Guodong Feng Lihui Lu Jihua Yan Lei School of Optics and Photonics Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing Institute of Technology Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Underwater image restoration is conducive to better underwater resource detection and information effective ***,the light in the complex water body diffusely reflective and the selection absorption of different band light results in blurring and color distortion of underwater ***,we propose a convolutional Dark Channel Prior(DCP)underwater image recovery algorithm to enhance underwater *** can do the pre-processing work for the subsequent YOLOv5 object *** enhancement algorithm first performs Commission International Eclairage Lab(CIELAB)equalization of underwater images for color distortion ***,the underwater image formation parameters are estimated by the minimum convolution region ***,Contrast Limited Adaptive Histogram Equalization(CLAHE) is performed to obtain an enhanced underwater ***,the enhanced underwater image is input to the YOLOv5 model for *** results show that the proposed method outperforms state-of-art algorithms in terms of image recovery effect,evaluation quality and detection accuracy.

关键词： image classification color equalization peak signal-to-noise ratio underwater color image quality evaluation

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A Compact Real-Time Thermal Imaging System Based on Heterogeneous System-on-Chip

A Compact Real-Time Thermal Imaging System Based on Heteroge...

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IEEE International conference on Embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Hyun Woo Oh Cheol-Ho Choi Jeong Woo Cha Hyunmin Choi Jung-Ho Shin Joon Hwan Han Infra Technology R&D Center Hanwha Systems Seongnam Republic of Korea

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. The image processing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640×480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280×1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar image processing algorithms.

关键词： Single instruction multiple data Noise Imaging systems architecture Organizations Real-time systems System-on-chip VLIW image enhancement Videos

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