BVTNet: Multi-label Multi-class Fusion of Visible and Thermal Camera for Free Space and Pedestrian Segmentation

John, Vijay; Boyali, Ali; Thompson, Simon G.; Mita, Seiichi

doi:10.1007/978-3-030-68780-9_24

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…Although free space detection can be realized based on camera or even LiDAR by semantic segmentation or neural network methods [7,8,9,10], approaches based on mmw radar is still in its very early stage because of the above mentioned shortcomings. From a different perspective, finely designed methods [5,11] proposed on free space detection recently are separated from improving data quality, leaving them impractical even Utopian in engineering.…”

Section: Introductionmentioning

confidence: 99%

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

Li,

Yu,

et al. 2024

Advances in Transdisciplinary Engineering

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In autonomous driving, free space detection based on 3+1D radar point cloud severely suffers from the existence of noises. To tackle this problem, we propose the novel non-data-driven Quasi-Density-Tree (QDTree) method, where no distance metrics are employed, and within the QDTree we use voting tool to analyse its nodes in terms of point cloud density or SNR, resulting noise data effectively filtered. We further split left point clouds into static and moving types according to Doppler to detect free space and Gaussian Mixture Method was used to generate safety confidence. The QDTree structure and point cloud dichotomy allow us to apply parallel computation in free space detection. Furthermore, the proposed free space detection method outperforms those state-of-the-art algorithms in terms of computation speed and result accuracy. Specifically, the proposed free space detection method consumes less than 30ms (with parallel computation, it is within 18ms) when processing 5-frame accumulated 4-corner-radar point cloud (∼5000 points) and hence it is adequate to meet real-time requirements.

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Section: Introductionmentioning

confidence: 99%

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

Li,

Yu,

et al. 2024

Advances in Transdisciplinary Engineering

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“…Recently, infrared images have been widely used in many real-time applications, such as pedestrian segmentation [ 1 ], salient object detection [ 2 ], object fusion tracking [ 3 ], and invisible clothing [ 4 ]. Compared with visible images, infrared images are also able to obtain temperature information of scenes and human bodies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Steganography Method for Infrared Image Based on Smooth Wavelet Transform and Convolutional Neural Network

Bai

et al. 2023

Sensors

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Infrared images have been widely used in many research areas, such as target detection and scene monitoring. Therefore, the copyright protection of infrared images is very important. In order to accomplish the goal of image-copyright protection, a large number of image-steganography algorithms have been studied in the last two decades. Most of the existing image-steganography algorithms hide information based on the prediction error of pixels. Consequently, reducing the prediction error of pixels is very important for steganography algorithms. In this paper, we propose a novel framework SSCNNP: a Convolutional Neural-Network Predictor (CNNP) based on Smooth-Wavelet Transform (SWT) and Squeeze-Excitation (SE) attention for infrared image prediction, which combines Convolutional Neural Network (CNN) with SWT. Firstly, the Super-Resolution Convolutional Neural Network (SRCNN) and SWT are used for preprocessing half of the input infrared image. Then, CNNP is applied to predict the other half of the infrared image. To improve the prediction accuracy of CNNP, an attention mechanism is added to the proposed model. The experimental results demonstrate that the proposed algorithm reduces the prediction error of the pixels due to full utilization of the features around the pixel in both the spatial and the frequency domain. Moreover, the proposed model does not require either expensive equipment or a large amount of storage space during the training process. Experimental results show that the proposed algorithm had good performances in terms of imperceptibility and watermarking capacity compared with advanced steganography algorithms. The proposed algorithm improved the PSNR by 0.17 on average with the same watermark capacity.

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RGB-T image analysis technology and application: A survey

Song

Zhao

Huang

et al. 2023

Engineering Applications of Artificial Intelligence

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BVTNet: Multi-label Multi-class Fusion of Visible and Thermal Camera for Free Space and Pedestrian Segmentation

Cited by 9 publications

References 13 publications

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

A Novel Steganography Method for Infrared Image Based on Smooth Wavelet Transform and Convolutional Neural Network

RGB-T image analysis technology and application: A survey

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