An Optimized Multi-sensor Fused Object Detection Method for Intelligent Vehicles

An accurate and efficient environment perception system is crucial for intelligent vehicles. This study proposes an optimized 2D object detection method utilizing multi-sensor fusion to improve the performance of the environment perception system. In the sensor fusion module, a depth completion network is used to predict dense depth map, so both dense and sparse RGB-D images can be obtained. Then, an efficient object detection baseline is optimized for intelligent vehicles. This method is verified by KITTI 2D object detection dataset. The experimental results show that the proposed method can be more accurate than many latest methods on KITTI leaderboard. Meanwhile, this method consumes less inference time and shows its high efficiency.

[1]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  P. Peixoto,et al.  Tracking and Classification of Dynamic Obstacles Using Laser Range Finder and Vision , 2006 .

[3]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[4]  Marius Leordeanu,et al.  Shift R-CNN: Deep Monocular 3D Object Detection With Closed-Form Geometric Constraints , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[5]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[7]  Marcelo H. Ang,et al.  A General Pipeline for 3D Detection of Vehicles , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Charles E. Thorpe,et al.  Perception for collision avoidance and autonomous driving , 2003 .

[9]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[11]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[12]  Henry Leung,et al.  Overview of Environment Perception for Intelligent Vehicles , 2017, IEEE Transactions on Intelligent Transportation Systems.

[13]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[15]  Steven L. Waslander,et al.  Confidence Guided Stereo 3D Object Detection with Split Depth Estimation , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[16]  Hujun Bao,et al.  Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Cristiano Premebida,et al.  LIDAR and vision‐based pedestrian detection system , 2009, J. Field Robotics.

[19]  Luc Van Gool,et al.  Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty , 2019, 2019 16th International Conference on Machine Vision Applications (MVA).

[20]  Leonidas J. Guibas,et al.  Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.