Detection and Recognition of Traffic Planar Objects Using Colorized Laser Scan and Perspective Distortion Rectification

Deng, Zhidong; Zhou, Lipu

doi:10.1109/tits.2017.2723902

Cited by 19 publications

(9 citation statements)

References 51 publications

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“…Summary: The traffic sign scans by LiDAR through the retro-reflective property, but lack of dense texture causes a difficulty to classify them [110,111]. The road lane and divider detection accuracy are enriched based on prior information and multi-model data, but the prior geometric information structure is insufficient to specify or streamline the global challenge.…”

Section: Traffic Sign Recognition (Tsr) Methodsmentioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

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Autonomous Driving Vehicle (ADV) services have become a prominent motif in intelligent vehicle technology by adapting deep learning features. Automated driverless services are a hercules task due to the dynamic driving environment and the performance is deliberately reliant on the quality of data fusion from sensors. Therefore, considering advanced 3D LiDAR sensors is essential to measure the surrounding with 360 • coverage. However, accomplishing maximum autonomy is the main challenge because of debilitated and complex driving environmenst. Deep Learning (DL) based models potentially impact surrounding measures for object detection, classification, and tracking. This study describes the importance of DL-LiDAR strategies to formulate ADV research challenges followed by a comprehensive analysis of Semantic Segmentation, Data Fusion, Data Representation, Feature Extraction, Dynamic Object Detection, and Autonomous Driving-Multi-Objective Tracking mechanisms. Unlike existing review papers, we examine various deep learning influenced approaches and describe their precarious measurements. Also, we analyze the impact of systematic methods based on open-source data sets for objectx (x=classification, localization, tracking) consolidation. Most of the summaries confine scalability, and feasibility of DL techniques in ADV to accomplish an intelligent driving system without human interference. Open research problems are discussed in the last section of the article.

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Section: Traffic Sign Recognition (Tsr) Methodsmentioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

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“…As an active vision, LIDAR can provide 3D geometric information, such as point clouds; yet, LIDAR cannot capture visual planar information. Data association of LIDAR and camera gives a promising direction for detection and recognition of traffic signs [83]. The structure of LIDAR and camera based system from [84] is shown in Fig.…”

Section: ) Data Cloud and Rgb Image Based Detectionmentioning

confidence: 99%

“…12. In this structure, For the data association of a laser point and its corresponding image pixel, parameters of both camera and LIDAR must be precomputed as a prerequisite [83]. Point clouds can provide geometric and localization information; whereas, digital images can provide detailed color, shape and texture information.…”

Section: ) Data Cloud and Rgb Image Based Detectionmentioning

confidence: 99%

Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives

et al. 2019

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Traffic signs recognition (TSR) is an important part of some advanced driver-assistance systems (ADASs) and auto driving systems (ADSs). As the first key step of TSR, traffic sign detection (TSD) is a challenging problem because of different types, small sizes, complex driving scenes, and occlusions. In recent years, there have been a large number of TSD algorithms based on machine vision and pattern recognition. In this paper, a comprehensive review of the literature on TSD is presented. We divide the reviewed detection methods into five main categories: color-based methods, shape-based methods, color-and shape-based methods, machine-learning-based methods, and LIDAR-based methods. The methods in each category are also classified into different subcategories for understanding and summarizing the mechanisms of different methods. For some reviewed methods that lack comparisons on public datasets, we reimplemented part of these methods for comparison. The experimental comparisons and analyses are presented on the reported performance and the performance of our reimplemented methods. Furthermore, future directions and recommendations of the TSD research are given to promote the development of the TSD.

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“…The traditional methods use machine learning algorithms, which need to extract features manually and then use a classifier for classification, such as Haar + AdaBoost [6][7][8], HOG + SVM [9,10] and so on. These methods generally have the following disadvantages: the feature requires manual extraction, resulting in a heavy workload and high time cost, and the detection effect is general.…”

Section: Introductionmentioning

confidence: 99%

MobileNet‐SSD MicroScope using adaptive error correction algorithm: real‐time detection of license plates on mobile devices

et al. 2020

IET Intelligent Transport Systems

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Detection and Recognition of Traffic Planar Objects Using Colorized Laser Scan and Perspective Distortion Rectification

Cited by 19 publications

References 51 publications

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives

MobileNet‐SSD MicroScope using adaptive error correction algorithm: real‐time detection of license plates on mobile devices

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