Robust Radar Detection and Classification of Traffic Vehicles Based on Anchor-free CenterNet

Guo, Zuyuan; Yi, Wei; Wu, Yuanfeng; Luo, Tai

doi:10.1109/ucet54125.2021.9674952

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…One prominent method is CenterNet, which employs a one-stage anchor-free architecture for precise localization and recognition of objects. [5] presented the application of CenterNet for traffic target detection and classification using radar data. The study simulated a large radar dataset with diverse types of traffic targets and evaluated the performance of CenterNet against conventional detection and classification algorithms.…”

Section: Centernetmentioning

confidence: 99%

Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance

Mohd Zubil,

Che Embi,

Ghauth

2024

JIWE

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With the ever-increasing number of vehicles on the road, a faster reliable security system for university entry is needed. This paper presents an approach for Automatic Number Plate Recognition (ANPR) using deep learning and PP-OCRv3. The proposed approach utilizes a pre-trained object detection model to locate license plates, extracts a single frame of the license plate, performs license plate recognition, applies pre-processing techniques, and employs PP-OCRv3 for text extraction in real time. The system was tested with Malaysian vehicle plates, and its accuracy and speed of detection were evaluated. The results show the system's potential to be easily adapted to different camera systems, angles, and lighting conditions by retraining the deep learning model. The paper also explores various deep learning methods, such as CenterNet, EfficientDet, and Faster R-CNN, and their effectiveness in automated vehicle registration detection. The research methodology involves creating a dataset from Open Images Dataset V4, converting label text into XML files, and utilizing the TensorFlow model trained on the COCO dataset. The paper concludes with the synthetic evaluation of the trained models, comparing their performance based on precision, recall, and F1-score. Overall, the proposed approach highlights the potential of deep learning and PP-OCRv3 in achieving accurate and efficient ANPR systems.

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Section: Centernetmentioning

confidence: 99%

Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance

Mohd Zubil,

Che Embi,

Ghauth

2024

JIWE

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“…In previous approaches, radar data is usually preprocessed before the information is transferred to an ANN in the form of Range-Doppler-Maps (RDMs) or other representations. Various applications of radar sensors are conceivable, ranging from distance determination and classification of objects in road traffic [11], to gesture recognition in consumer electronics [12] and even breath and heartbeat frequency detection of humans [13]. There is also an increasing trend to move the previous data preprocessing steps to the ANN as well, [14] as this allows specialized AI accelerators to shorten response times and enable processing on the edge [15], [16].…”

Section: B Aimentioning

confidence: 99%

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Valtl

Issakov

2022

IEEE Access

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This work is a result of a collaboration between the projects "KI-Flex" (project number 16ES1027), funded by the German Federal Ministry of Education and Research (BMBF) within the founding program Microelectronic from Germany innovation driver, the "KoSi" project, (project number 01MM20011C) funded by the German Federal Ministry for Digital and Transport (BMDV) within the funding program "Automatisiertes, Vernetztes Fahren" and the project "TEACHING" (project number 871385) founded by the Horizon 2020 program."ABSTRACT As more and more applications rely on Artificial Intelligence (AI), it is inevitable to explore the associated safety and security risks, especially for sensitive applications where physical integrity is at risk. One of the most interesting challenges that come with AI are adversarial attacks being a wellresearched problem in the visual domain, where a small change in the input data can cause the Neural Network (NN) to make an incorrect prediction. In the radar domain, AI is not that widespread yet but the results that AI applications produce are very promising, which is why more and more applications based on it are being used. This work presents three possible attack methods that are particularly suitable for the radar domain. The developed algorithms generate universal adversarial attack patches for all sorts of radar applications based on NN. The main goal of the algorithms, apart from the computation of universal patches, is the identification of sensitive areas in the raw radar data input which than can be examined more closely. To the best of our knowledge, this is the first work that deals with calculating universal patches on raw radar data, which is of great importance especially for interference analysis. The developed algorithms have been verified on two data sets. One in the field of autonomous driving where the attacks lead to a steering misprediction of up to 0.3 for the steering value which is within [-1,1], with the results also being successfully tested on a demonstrator. The other data set originated from a gesture recognition task, where the attacks decreased the accuracy, originally at 97.0% up to a minimum of 16.5%, which is slightly above 12.5% being the accuracy for a purely random prediction.INDEX TERMS adversarial attacks, artificial neural networks, autonomous vehicles, edge computing, object recognition, radar applications, real-time systems

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Deep learning-based circular disk type radar target detection in complex environment

Das

Swarna

Hossain

2023

Physical Communication

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Robust Radar Detection and Classification of Traffic Vehicles Based on Anchor-free CenterNet

Cited by 5 publications

References 15 publications

Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance

Assessing the Efficiency of Deep Learning Methods for Automated Vehicle Registration Recognition for University Entrance

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Deep learning-based circular disk type radar target detection in complex environment

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