Learning Approach with Random Forests on Vehicle Detection

Wang, Liwen; Yang, Xuefei; Siu, Wan-Chi

doi:10.1109/icdsp.2018.8631871

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…To enhance the accuracy of vehicle detection, methods based on feature extraction using techniques such as the histogram of oriented gradient (HOG) method [22], Haar-like features [23], and local binary patterns (LBPs) [24] have been proposed. To further improve the robustness and generalization of vehicle recognition, methods employing machine learning algorithms, such as artificial neural networks [25], Adaboost [26,27], random forest [28], and naïve Bayes [29], have also been introduced.…”

Section: Methods Based On Digital Image Processing and Machine Learningmentioning

confidence: 99%

Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Liu,

Zhang,

Zhou

et al. 2023

Electronics

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Effective vehicle detection plays a crucial role in various applications in cities, including traffic management, urban planning, vehicle transport, and surveillance systems. However, existing vehicle detection methods suffer from low recognition accuracy, high computational costs, and excessive parameters. To address these challenges, this paper proposed a high-precision and lightweight detector along with a new dataset for construction-related vehicles. The dataset comprises 8425 images across 13 different categories of vehicles. The detector was based on a modified version of the You Only Look Once (YOLOv4) algorithm. DenseNet was utilized as the backbone to optimize feature transmission and reuse, thereby improving detection accuracy and reducing computational costs. Additionally, the detector employed depth-wise separable convolutions to optimize the model structure, specifically focusing on the lightweight neck and head components. Furthermore, H-swish was used to enhance non-linear feature extraction. The experimental results demonstrated that the proposed detector achieves a mean average precision (mAP) of 96.95% on the provided dataset, signifying a 4.03% improvement over the original YOLOv4. The computational cost and parameter count of the detector were 26.09GFLops and 16.08 MB, respectively. The proposed detector not only achieves lower computational costs but also provides higher detection when compared to YOLOv4 and other state-of-the-art detectors.

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Section: Methods Based On Digital Image Processing and Machine Learningmentioning

confidence: 99%

Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Liu,

Zhang,

Zhou

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Likewise, for a forward collision-avoidance assistance (FCAA) or forward collision warning (FCW) system, some research works, including [150], [151], and [152], focused on an RF algorithm, while works [153], [154], and [155] emphasized SVM. In another study, [156], the authors simplified trajectory planning by solving a convex optimization problem formulated as an SVM, resulting in a trajectory planner-friendly, obstacle-free corridor.…”

Section: E State-of-the-art Machine Learning Algorithms For Adas Appl...mentioning

confidence: 99%

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar

Hong

Moore

2023

IEEE Open J. Veh. Technol.

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Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

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“…Random forest algorithm is an algorithm for classifcation and prediction, which uses the bootstrap resampling method to draw multiple samples from the original sample model decision trees for each bootstrap sample and then combine the predictions of multiple decision trees to arrive at the fnal prediction result by voting and has high prediction accuracy, good tolerance for outliers and noise, and is not prone to overftting [32]. Applications of random forest in the feld of assisted driving include the detection of trains ahead to avoid collisions [33] and the monitoring of driver emotions [34]. Considering that the process of random forest algorithm implementation is to set up diferent weights for diferent decision trees to complete the voting to arrive at the fnal result.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

The Design of Vehicle Profile Based on Multivehicle Collaboration for Autonomous Vehicles in Roundabouts

Cao

Liu

et al. 2023

Security and Communication Networks

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The collaborative strategy of vehicle-road-environment based on intelligent and connected vehicles (ICVs) to assist in driving vehicles safely and relieve traffic congestion has become an effective solution. This paper proposed a strategy for vehicle lane change and roundabouts traffic based on vehicle profile (VP) in combination with the driving characteristics of roundabouts. Initially, in order to solve the confusion problem of multisource heterogeneous data of ICVs in roundabouts, this paper defines VP to describe and characterize the multidimensional data of ICVs, so the data in ICVs can be further applied. Furthermore, the weights of relevant parameters in the VP are updated based on the random forest algorithm. In addition, the payoff function is designed for the lane change decision at the exit of roundabouts based on the VP and dynamic weights. Finally, the performance of the proposed algorithm is compared with other algorithms through the SUMO platform and three scenarios are used in the simulation verification, including traffic congestion, normal, and sparse. The experimental results verify the optimization effect of vehicle profile on roundabout traffic strategy and also show that this algorithm can effectively improve the efficiency of vehicle traffic in roundabouts. In particular, the efficiency and comfort of vehicles in roundabouts are effectively improved in normal traffic scenarios.

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Learning Approach with Random Forests on Vehicle Detection

Cited by 5 publications

References 15 publications

Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

The Design of Vehicle Profile Based on Multivehicle Collaboration for Autonomous Vehicles in Roundabouts

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