Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles

Autonomous vehicles require accurate, and fast decision-making perception systems to know the driving environment. The 2D object detection is critical in allowing the perception system to know the environment. However, 2D object detection lacks depth information, which are crucial for understanding...

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Vydané v:Soft computing (Berlin, Germany) Ročník 27; číslo 23; s. 18195 - 18213
Hlavní autori: Wu, Qingyu, Li, Xiaoxiao, Wang, Kang, Bilal, Hazrat
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023
Springer Nature B.V
Predmet:
Application of Soft Computing
Artificial Intelligence
Artificial neural networks
Automobile safety
Autonomous vehicles
Cameras
Computational Intelligence
Control
Driving
Engineering
Feature extraction
Feature maps
Frames per second
Human error
Lasers
Lidar
Mathematical Logic and Foundations
Mechatronics
Methods
Neural networks
Object recognition
Perception
Planning
Robotics
Semantics
Sensors
Telematics
Traffic
Feature extraction
CNN
Autonomous vehicle
3D LIDAR
Regional features
Object detection
ISSN:1432-7643, 1433-7479
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Popis
Shrnutí:Autonomous vehicles require accurate, and fast decision-making perception systems to know the driving environment. The 2D object detection is critical in allowing the perception system to know the environment. However, 2D object detection lacks depth information, which are crucial for understanding the driving environment. Therefore, 3D object detection is essential for the perception system of autonomous vehicles to predict the location of objects and understand the driving environment. The 3D object detection also faces challenges because of scale changes, and occlusions. Therefore in this study, a novel object detection method is presented that fuses the complementary information of 2D and 3D object detection to accurately detect objects in autonomous vehicles. Firstly, the aim is to project the 3D-LiDAR data into image space. Secondly, the regional proposal network (RPN) to produce a region of interest (ROI) is utilised. The ROI pooling network is used to map the ROI into ResNet50 feature extractor to get a feature map of fixed size. To accurately predict the dimensions of all the objects, we fuse the features of the 3D-LiDAR with the regional features obtained from camera images. The fused features from 3D-LiDAR and camera images are employed as input to the faster-region based convolution neural network (Faster-RCNN) network for the detection of objects. The assessment results on the KITTI object detection dataset reveal that the method can accurately predict car, van, truck, pedestrian and cyclist with an average precision of 94.59%, 82.50%, 79.60%, 85.31%, 86.33%, respectively, which is better than most of the previous methods. Moreover, the average processing time of the proposed method is only 70 ms which meets the real-time demand of autonomous vehicles. Additionally, the proposed model runs at 15.8 frames per second (FPS), which is faster than state-of-the-art fusion methods for 3D-LiDAR and camera.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09278-3