Cascade saccade machine learning network with hierarchical classes for traffic sign detection

Liu, Zhanwen; Qi, Mingyuan; Shen, Chao; Fang, Yong; Zhao, Xiangmo

doi:10.1016/j.scs.2020.102700

Cited by 38 publications

(19 citation statements)

References 27 publications

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“…It should be also noted that the standard deviations, when compared to median value and range of the variable, are relatively low -except in the case of our output, residuary resistance per unit weight of displacement. This can indicate that the data in question has a relatively uneven distribution across its range, meaning that more data points are located on one end of the range [19,20]. This can be confirmed by viewing the distributions of the variables, by plotting the histograms of the data, which is shown in Figure 1.…”

Section: Datasetmentioning

confidence: 72%

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

Šegota

Lorencin

Šercer³

et al. 2021

Pomorstvo (Online)

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Determining the residuary resistance per unit weight of displacement is one of the key factors in the design of vessels. In this paper, the authors utilize two novel methods – Symbolic Regression (SR) and Gradient Boosted Trees (GBT) to achieve a model which can be used to calculate the value of residuary resistance per unit weight, of displacement from the longitudinal position of the center of buoyancy, prismatic coefficient, length-displacement ratio, beam-draught ratio, length-beam ratio, and Froude number. This data is given as results of 308 experiments provided as a part of a publicly available dataset. The results are evaluated using the coefficient of determination (R2) and Mean Absolute Percentage Error (MAPE). Pre-processing, in the shape of correlation analysis combined with variable elimination and variable scaling, is applied to the dataset. The results show that while both methods achieve regression results, the result of regression of SR is relatively poor in comparison to GBT. Both methods provide slightly poorer, but comparable results to previous research focussing on the use of “black-box” methods, such as neural networks. The elimination of variables does not show a high influence on the modeling performance in the presented case, while variable scaling does achieve better results compared to the models trained with the non-scaled dataset.

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Section: Datasetmentioning

confidence: 72%

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

Šegota

Lorencin

Šercer³

et al. 2021

Pomorstvo (Online)

View full text Add to dashboard Cite

show abstract

“…However, LiDARs are good at mapping its vicinity [127], though this solution also has drawbacks that primarily include LiDAR performance under low contrasts and difficult angles [128]. For such cases, AI-based features turn out to be most useful [129] [130] [131].…”

Section: B Frequency-modulated Continuous Wave (Fmcw) Radars In Adasmentioning

confidence: 99%

“…RADAR sensors also have some limitations when regarding the classification process as it is difficult for an automotive RADAR to identify and classify small objects like signboards. For such tasks, other sensors and AI-based features processing turn out to be most useful [129] [130] [131].…”

Section: Challenges and Issues In Sensor Fusionmentioning

confidence: 99%

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

et al. 2022

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The automotive industry is transitioning towards intelligent, connected, and autonomous vehicles to avoid traffic congestion, conflicts, and collisions with increased driver safety. Connected and autonomous vehicles (CAV) must be aware of their surroundings and act as per their environment. Communication infrastructure can be vital in transmitting necessary information to peers and receiving critical information for timely decisions. This article provides a comprehensive review of the topic, covering the aspects of enabling wireless technologies and sensor fusion. The article reviews data acquisition using various sensing devices such as RADAR (Radio Detection and Ranging), LiDAR (Light Detection and Ranging), cameras, and multi-modal sensor fusion of the acquired data after signal processing. Thereafter, it reviews the communication and networking infrastructure for intra-and inter-vehicle communication and related technologies. For each of these themes, research challenges and future directions have been identified. INDEX TERMSSensor fusion, inter-vehicle communication, intra-vehicle communication, environment sensing, road safety, internet of vehicles, connected and autonomous vehicles, 5G networks I. INTRODUCTION

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“…Many researchers have attempted to increase the accuracy of traffic sign recognition using a variety of methods [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In general, there are three categories of traffic signs in China: indication, warning and prohibition, which are represented by blue, yellow and red, respectively.…”

Section: Related Workmentioning

confidence: 99%

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Liu

Cai

Tang

et al. 2022

Entropy

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Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.

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Cascade saccade machine learning network with hierarchical classes for traffic sign detection

Cited by 38 publications

References 27 publications

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

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