Traffic Image Dehazing Based on Wavelength Related Physical Imaging Model

Wang, Yibin; Yin, Shu; Zheng, Jianming

doi:10.1007/978-3-030-54407-2_34

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2020

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“…The defogging picture obtained by this method has higher contrast and color consistency. Wang [9] focused the research on how to solve the problem of uneven distribution of fog concentration. According to the correlation between wavelength and fog concentration, they built a wave-related physical imaging model suitable for traffic monitoring images.…”

Section: B Preprocessingmentioning

confidence: 99%

An Overview Of 3D Object Detection

Wang¹,

Ye²

2020

Preprint

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Point cloud 3D object detection has recently received major attention and becomes an active research topic in 3D computer vision community. However, recognizing 3D objects in LiDAR (Light Detection and Ranging) is still a challenge due to the complexity of point clouds. Objects such as pedestrians, cyclists, or traffic cones are usually represented by quite sparse points, which makes the detection quite complex using only point cloud. In this project, we propose a framework that uses both RGB and point cloud data to perform multiclass object recognition. We use existing 2D detection models to localize the region of interest (ROI) on the RGB image, followed by a pixel mapping strategy in the point cloud, and finally, lift the initial 2D bounding box to 3D space. We use the recently released nuScenes dataset-a large-scale dataset contains many data formats-to training and evaluate our proposed architecture.

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Section: B Preprocessingmentioning

confidence: 99%