Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment

By Hongbo Gao, Bo Cheng, Jianqiang Wang, Keqiang Li, Jianhui Zhao, Deyi Li. IEEE Transactions on Industrial Informatics, 2018

442 citations recorded by the source corpus.

This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is also adopted to guarantee both object classification accuracy and minimal loss. Experimental results are presented and show the effectiveness and efficiency of object classification strategies.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Instrumentation

View Paper: https://www.semanticscholar.org/paper/9e46672b07504f7ff677dff323bb4c0efecb9091

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Wei Zhong
2. Xin Fan
3. Zhongxuan Luo
4. Fan Liu
5. Shouheng Peng
6. Jinyuan Liu
7. Yu Wu
8. Zewen Li

Citations in the corpus

• DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection (2022, background): “a) In early fusion, raw data from different sensors are directly transferred and fused [9].”
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection (2022, background): “Multi-modality imaging has attracted significant attention in a wide range of applications, e.g. , surveillance [28] and autonomous driving [5], with the rapid development of sensing hardware.”
• Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, unknown): “As an attempt to improve depth estimation, researchers in [82,83] trained a deep learning CNN network to estimate depth using a single monocular camera.”
• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, background): “A lot of impressive applications of CNN are emerging in modern society, from simple cat–dog classifiers [190] to sophisticated autonomous vehicles [189], [191], [192].”
• Multi-Sensor Fusion in Automated Driving: A Survey (2020, methodology): “Reference [102] directly spliced the up-sampled point-cloud data of LiDAR with the image as the input of AlexNet, to ensure that the input of the CNN contains the image of depth information and obtain more accurate results.”