Deep Learning Methods for Object Detection in Autonomous Vehicles

Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem solving in object detection algorithms. We have discussed various object detection methods and challenges in this paper.

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

Journal of Imaging ◽

10.3390/jimaging7080145 ◽

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.

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

ICT Express ◽

10.1016/j.icte.2021.12.016 ◽

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

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Multiple Object Detection Based on Clustering and Deep Learning Methods

Sensors ◽

10.3390/s20164424 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4424

Author(s):

Huu Thu Nguyen ◽

Eon-Ho Lee ◽

Chul Hee Bae ◽

Sejin Lee

Keyword(s):

Deep Learning ◽

Object Detection ◽

Spatial Clustering ◽

Clustering Algorithms ◽

Three Dimensional ◽

Autonomous Driving ◽

Learning Methods ◽

Multiple Object ◽

Multiple Object Detection ◽

Negative Effect

Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.

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

International Journal of Electrical Engineering Education ◽

10.1177/0020720920983524 ◽

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.

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