Title: ResNet-based surface normal estimator with multilevel fusion approach with adaptive median filter region growth algorithm for road scene segmentation

Authors: Yachao Zhang; Yuxia Yuan

Addresses: School of Electronic and Electrical Engineering, Zhengzhou University of Science and Technology, China ' School of Electronic and Electrical Engineering, Zhengzhou University of Science and Technology, China

Abstract: As an integral part of information processing, road information has important application value in map drawing, post-disaster rescue and military application. In this paper, convolutional neural network is used to fuse lidar point cloud and image data to achieve road segmentation in traffic scenes. We first use adaptive median filter region growth algorithm to preprocess the input image. The semantic segmentation convolutional neural network with encoding and decoding structure of ResNet is used as the basic network to cross and fuse the point cloud surface normal features and RGB image features at different levels. After fusion, the data is restored into the decoder. Finally, the detection result is obtained by activation function. The KITTI data set is used for evaluation. Experimental results show that the proposed fusion scheme has the best segmentation performance. Compared with other road detection methods, the results show that the proposed method can achieve better overall performance. In terms of AP, the value of proposed method exceeds 95% for UM, UMM scene.

Keywords: road segmentation; adaptive median filter region growth; data fusion; point cloud surface normal feature; encoding and decoding structure.

DOI: 10.1504/IJCVR.2024.135128

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.1, pp.99 - 117

Received: 12 May 2022
Accepted: 05 Jul 2022
Published online: 01 Dec 2023 *