scholarly journals A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection

ICT Express ◽  
2022 ◽  
Author(s):  
Ji Dong Choi ◽  
Min Young Kim
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Infrared Camera ◽  
Thermal Infrared ◽  
Fusion System

scholarly journals Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

2021 ◽  
Vol 7 (8) ◽  
pp. 145
Author(s):  
Antoine Mauri ◽  
Redouane Khemmar ◽  
Benoit Decoux ◽  
Madjid Haddad ◽  
Rémi Boutteau
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Video Game ◽  
Autonomous Vehicles ◽  
Object Localization ◽  
Driver Assistance Systems ◽  
Smart Mobility ◽  
Bounding Boxes ◽  
Detection And Localization

For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI’s road dataset as well as on our own hybrid virtual road/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.

Improved YOLOv3 with duplex FPN for object detection based on deep learning

2021 ◽  
pp. 002072092098352
Author(s):  
Seokyong Shin ◽  
Hyunho Han ◽  
Sang Hun Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Detection Accuracy ◽  
Small Object ◽  
Feature Maps ◽  
Low Level ◽  
Small Object Detection ◽  
High Level ◽  
Networks Structure

YOLOv3 is a deep learning-based real-time object detector and is mainly used in applications such as video surveillance and autonomous vehicles. In this paper, we proposed an improved YOLOv3 (You Only Look Once version 3) applied Duplex FPN, which enhanced large object detection by utilizing low-level feature information. The conventional YOLOv3 improved the small object detection performance by applying FPN (Feature Pyramid Networks) structure to YOLOv2. However, YOLOv3 with an FPN structure specialized in detecting small objects, so it is difficult to detect large objects. Therefore, this paper proposed an improved YOLOv3 applied Duplex FPN, which can utilize low-level location information in high-level feature maps instead of the existing FPN structure of YOLOv3. This improved the detection accuracy of large objects. Also, an extra detection layer was added to the top-level feature map to prevent failure of detection of parts of large objects. Further, dimension clusters of each detection layer were reassigned to learn quickly how to accurately detect objects. The proposed method was compared and analyzed in the PASCAL VOC dataset. The experimental results showed that the bounding box accuracy of large objects improved owing to the Duplex FPN and extra detection layer, and the proposed method succeeded in detecting large objects that the existing YOLOv3 did not.

scholarly journals A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection

2019 ◽  
Author(s):  
Felix Nobis ◽  
Maximilian Geisslinger ◽  
Markus Weber ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Sensor Fusion ◽  
Camera Sensor

Object Detection Using Multi-Sensor Fusion Based on Deep Learning

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Taohua Zhou ◽  
Kun Jiang ◽  
Zhongyang Xiao ◽  
Chunlei Yu ◽  
Diange Yang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Sensor Fusion

scholarly journals Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

2017 ◽  
Author(s):  
Raja Sekhar Dheekonda ◽  
Sampad Panda ◽  
Md Nazmuzzaman khan ◽  
Mohammad Hasan ◽  
Sohel Anwar
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Sensor Fusion ◽  
Fusion Algorithm ◽  
Learning Network ◽  
Deep Learning Network
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