Machine learning based automatic target recognition algorithm applicable to ground penetrating radar data

Abeynayake, Canicious; Son, Vidar; Shovon, Md. Hedayetul Islam; Yokohama, Hiroshi

doi:10.1117/12.2517195

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Object detection algorithms can be classified into two main categories: non-neural network based and neural network based. Non-neural network-based object detection algorithms define and detect features using techniques such as Scale-Invariant Feature Transform (SIFT) or Histogram of Oriented Gradients (HOG) then apply a classification algorithm such as Support Vector Machine (SVM) to classify the objects [8][9][10] [11]. Neural-network based techniques do not need to define the features and are usually based on convolutional neural networks.…”

Section: Relevant Previous Workmentioning

confidence: 99%

A comparative analysis of deep learning algorithms for optical drone detection

Shovon

Gopalan

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Recent events in both armed conflict and the civil aviation space continue to highlight the threat of Unmanned Aerial Systems (UAS), often referred to as drones. Most drone counter measure systems and all early warning systems require drone detection. A number of drone detection techniques, including radars, RF signal capture and optical sensing have been developed to provide this capability. These techniques all have different advantages and disadvantages and a robust counter UAS (C-UAS) or UAS early warning system should combine several of these systems. One of the available detection systems is computer vision using deep learning and optical sensors. Due to the rapid advancement of this area, there are many options for practitioners seeking to utilize cutting edge deep learning techniques for optical UAS detection. In this study, we provide a comparative performance analysis of four state-of-the-art deep learning-based object detection algorithms, namely YOLOv5 small and large, SSD and Faster RCNN. We show that the YOLOv5 based models and Faster RCNN model are very close to each other in terms of accuracy while they outperform SSD. The YOLOv5 based models are also significantly faster than both SSD and Faster RCNN algorithms. Our analysis suggests that among the investigated algorithms, YOLOv5 small provides the best trade-off between accuracy and speed for a C-UAS self-protection or early warning system.

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Section: Relevant Previous Workmentioning

confidence: 99%

A comparative analysis of deep learning algorithms for optical drone detection

Shovon

Gopalan

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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“…In general, ML and DL-based approaches have improved significantly over the past decade, driven by the availability of advanced GPUs accelerated by its highly parallel architectures. The application of ML-based perception algorithms in optical systems for object recognition and classification is already being used in commercial systems [ 28 ] and its feasibility to interpret radar returns was demonstrated in [ 29 ] and for target classification in [ 30 , 31 ]. Furthermore, the authors in [ 32 ] proposed an ML-based method for the perception of airborne radar and compared its performance with that of a DL algorithm based on recurrent neural networks (RNNs).…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

Magosi

Eichberger

2023

Sensors

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Despite the progress in driving automation, the market introduction of higher-level automation has not yet been achieved. One of the main reasons for this is the effort in safety validation to prove functional safety to the customer. However, virtual testing may compromise this challenge, but the modelling of machine perception and proving its validity has not been solved completely. The present research focuses on a novel modelling approach for automotive radar sensors. Due to the complex high-frequency physics of radars, sensor models for vehicle development are challenging. The presented approach employs a semi-physical modelling approach based on experiments. The selected commercial automotive radar was applied in on-road tests where the ground truth was recorded with a precise measurement system installed in ego and target vehicles. High-frequency phenomena were observed and reproduced in the model on the one hand by using physically based equations such as antenna characteristics and the radar equation. On the other hand, high-frequency effects were statistically modelled using adequate error models derived from the measurements. The model was evaluated with performance metrics developed in previous works and compared to a commercial radar sensor model. Results show that, while keeping real-time performance necessary for X-in-the-loop applications, the model is able to achieve a remarkable fidelity as assessed by probability density functions of the radar point clouds and using the Jensen–Shannon divergence. The model delivers values of radar cross-section for the radar point clouds that correlate well with measurements comparable with the Euro NCAP Global Vehicle Target Validation process. The model outperforms a comparable commercial sensor model.

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“…Machine learning is also used to learn important data structures from the radar data acquired for different moving targets. Many papers have been presented in the literature for radar target recognition using machine learning methods [ 81 , 82 , 83 ]. For instance, the author of [ 81 ] presented a classification of airborne targets based on a supervised machine learning algorithm (SVM and Naïve Bayes).…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

“…Airborne radar was used to provide the measurements of the aerial, sea surface, and ground moving targets. C. Abeynayake et al [ 82 ] developed an automatic target recognition approach based on a machine learning algorithm applied to ground penetration radar data. Their system helps detect complex features that are relevant to a multitude of thread objects.…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

Zhang

et al. 2021

Sensors

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The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

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Machine learning based automatic target recognition algorithm applicable to ground penetrating radar data

Cited by 3 publications

References 6 publications

A comparative analysis of deep learning algorithms for optical drone detection

A comparative analysis of deep learning algorithms for optical drone detection

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

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