Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

Interference Mitigation ◽

Continuous Wave ◽

Driver Assistance Systems ◽

Data Set ◽

Radar Sensors ◽

Radar Systems ◽

Autonomous Cars

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

10.36227/techrxiv.11919102.v3 ◽

2020 ◽

Author(s):

Nicolae-Catalin Ristea ◽

Andrei Anghel ◽

Radu Tudor Ionescu

Keyword(s):

Neural Networks ◽

Interference Mitigation ◽

Continuous Wave ◽

Driver Assistance Systems ◽

Data Set ◽

Radar Sensors ◽

Radar Systems ◽

Autonomous Cars

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

10.36227/techrxiv.11919102.v2 ◽

2020 ◽

Author(s):

Nicolae-Catalin Ristea ◽

Andrei Anghel ◽

Radu Tudor Ionescu

Keyword(s):

Neural Networks ◽

Interference Mitigation ◽

Continuous Wave ◽

Driver Assistance Systems ◽

Data Set ◽

Radar Sensors ◽

Radar Systems ◽

Autonomous Cars

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) ◽

10.1109/vtc2020-fall49728.2020.9348690 ◽

2020 ◽

Author(s):

Nicolae-Catalin Ristea ◽

Andrei Anghel ◽

Radu Tudor Ionescu

Keyword(s):

Neural Networks ◽

Fully Convolutional Neural Networks

Interference Mitigation ◽

Automotive Radar ◽

Automatic Gully Detection: Neural Networks and Computer Vision

Remote Sensing ◽

10.3390/rs12111743 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1743

Author(s):

Artur M. Gafurov ◽

Oleg P. Yermolayev

Keyword(s):

Neural Network ◽

Neural Networks ◽

Large Scale ◽

Satellite Images ◽

Gully Erosion ◽

Training Data ◽

Russian Plain ◽

Data Set ◽

High Resolution Satellite Images

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

Fully Convolutional Neural Networks for Large Scale Cropland Mapping with Historical Label Dataset

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519554 ◽

2018 ◽

Author(s):

Dujuan Zhang ◽

Jinshui Zhang ◽

Yaozhong Pan ◽

Yaming Duan

Keyword(s):

Neural Networks ◽

Large Scale ◽

Cropland Mapping

GeoBoost: An Incremental Deep Learning Approach toward Global Mapping of Buildings from VHR Remote Sensing Images

Remote Sensing ◽

10.3390/rs12111794 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1794

Author(s):

Naisen Yang ◽

Hong Tang

Keyword(s):

Neural Networks ◽

Large Scale ◽

Semantic Segmentation ◽

Data Sets ◽

Data Set ◽

Large Scale Data ◽

Learning Tasks ◽

Global Mapping ◽

Scale Data

Modern convolutional neural networks (CNNs) are often trained on pre-set data sets with a fixed size. As for the large-scale applications of satellite images, for example, global or regional mappings, these images are collected incrementally by multiple stages in general. In other words, the sizes of training datasets might be increased for the tasks of mapping rather than be fixed beforehand. In this paper, we present a novel algorithm, called GeoBoost, for the incremental-learning tasks of semantic segmentation via convolutional neural networks. Specifically, the GeoBoost algorithm is trained in an end-to-end manner on the newly available data, and it does not decrease the performance of previously trained models. The effectiveness of the GeoBoost algorithm is verified on the large-scale data set of DREAM-B. This method avoids the need for training on the enlarged data set from scratch and would become more effective along with more available data.

A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields

Agris on-line Papers in Economics and Informatics ◽

10.7160/aol.2021.130104 ◽

2021 ◽

Vol 13 (1) ◽

pp. 49-57

Author(s):

Brahim Jabir ◽

Noureddine Falih ◽

Asmaa Sarih ◽

Adil Tannouche

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Sugar Beet ◽

Precision Agriculture ◽

Large Scale ◽

Aerial Images ◽

Data Set ◽

High Level ◽

And Control

Researchers in precision agriculture regularly use deep learning that will help growers and farmers control and monitor crops during the growing season; these tools help to extract meaningful information from large-scale aerial images received from the field using several techniques in order to create a strategic analytics for making a decision. The information result of the operation could be exploited for many reasons, such as sub-plot specific weed control. Our focus in this paper is on weed identification and control in sugar beet fields, particularly the creation and optimization of a Convolutional Neural Networks model and train it according to our data set to predict and identify the most popular weed strains in the region of Beni Mellal, Morocco. All that could help select herbicides that work on the identified weeds, we explore the way of transfer learning approach to design the networks, and the famous library Tensorflow for deep learning models, and Keras which is a high-level API built on Tensorflow.

Deep convolutional neural networks as strong gravitational lens detectors

Astronomy and Astrophysics ◽

10.1051/0004-6361/201731201 ◽

2018 ◽

Vol 611 ◽

pp. A2 ◽

Cited By ~ 17

Author(s):

C. Schaefer ◽

M. Geiger ◽

T. Kuntzer ◽

J.-P. Kneib

Keyword(s):

Neural Networks ◽

Large Scale ◽

Characteristic Curve ◽

Simulated Data ◽

Large Data ◽

Gravitational Lens ◽

Deep Convolutional Neural Networks ◽

Data Set ◽

Strong Lensing

Context. Future large-scale surveys with high-resolution imaging will provide us with approximately 105 new strong galaxy-scale lenses. These strong-lensing systems will be contained in large data amounts, however, which are beyond the capacity of human experts to visually classify in an unbiased way. Aims. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the strong-lensing challenge organized by the Bologna Lens Factory. It achieved first and third place, respectively, on the space-based data set and the ground-based data set. The goal was to find a fully automated lens finder for ground-based and space-based surveys that minimizes human inspection. Methods. We compared the results of our CNN architecture and three new variations (“invariant” “views” and “residual”) on the simulated data of the challenge. Each method was trained separately five times on 17 000 simulated images, cross-validated using 3000 images, and then applied to a test set with 100 000 images. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score, and the recall with no false positive (Recall0FP). Results. For ground-based data, our best method achieved an AUC score of 0.977 and a Recall0FP of 0.50. For space-based data, our best method achieved an AUC score of 0.940 and a Recall0FP of 0.32. Adding dihedral invariance to the CNN architecture diminished the overall score on space-based data, but achieved a higher no-contamination recall. We found that using committees of five CNNs produced the best recall at zero contamination and consistently scored better AUC than a single CNN. Conclusions. We found that for every variation of our CNN lensfinder, we achieved AUC scores close to 1 within 6%. A deeper network did not outperform simpler CNN models either. This indicates that more complex networks are not needed to model the simulated lenses. To verify this, more realistic lens simulations with more lens-like structures (spiral galaxies or ring galaxies) are needed to compare the performance of deeper and shallower networks.

Accurate Neuronal Soma Segmentation Using 3D Multi-Task Learning U-Shaped Fully Convolutional Neural Networks

Frontiers in Neuroanatomy ◽

10.3389/fnana.2020.592806 ◽

2021 ◽

Vol 14 ◽

Author(s):

Tianyu Hu ◽

Xiaofeng Xu ◽

Shangbin Chen ◽

Qian Liu

Keyword(s):

Neural Networks ◽

Quantitative Analysis ◽

Large Scale ◽

Neuronal Morphology ◽

Imaging Data ◽

Neuronal Somata ◽

Task Learning ◽

Neuronal Soma

Neuronal soma segmentation is a crucial step for the quantitative analysis of neuronal morphology. Automated neuronal soma segmentation methods have opened up the opportunity to improve the time-consuming manual labeling required during the neuronal soma morphology reconstruction for large-scale images. However, the presence of touching neuronal somata and variable soma shapes in images brings challenges for automated algorithms. This study proposes a neuronal soma segmentation method combining 3D U-shaped fully convolutional neural networks with multi-task learning. Compared to existing methods, this technique applies multi-task learning to predict the soma boundary to split touching somata, and adopts U-shaped architecture convolutional neural network which is effective for a limited dataset. The contour-aware multi-task learning framework is applied to the proposed method to predict the masks of neuronal somata and boundaries simultaneously. In addition, a spatial attention module is embedded into the multi-task model to improve neuronal soma segmentation results. The Nissl-stained dataset captured by the micro-optical sectioning tomography system is used to validate the proposed method. Following comparison to four existing segmentation models, the proposed method outperforms the others notably in both localization and segmentation. The novel method has potential for high-throughput neuronal soma segmentation in large-scale optical imaging data for neuron morphology quantitative analysis.