Fan Li^1,2, Shuyao Zhang³, Jie Yang^1,2,*, Zhicheng Feng^1,2, Zhichao Chen^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525 - 12 September 2024

Abstract Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction… More >

Peicheng Shi^1,*, Zhiqiang Liu¹, Heng Qi¹, Aixi Yang²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5615-5637, 2023, DOI:10.32604/cmc.2023.037794 - 29 April 2023

Abstract In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the… More >

Xiaorui Zhang^1,2,3,4,*, Ziquan Zhao¹, Wei Sun^4,5, Qi Cui⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 537-550, 2023, DOI:10.32604/csse.2023.034230 - 20 January 2023

Abstract LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox). However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning. To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate… More >

Peng Chen¹, Weiwei Zhang^1,*, Ziyao Xiao¹, Yongxiang Tian²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 97-109, 2022, DOI:10.32604/cmes.2022.016632 - 29 November 2021

Abstract Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving. This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively. Using 2D region proposals in an RGB image, this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network (FPS-Net) and feature extraction network (FE-Net). Subsequently, the encoder-decoder network (ED-Net) implements 3D-oriented bounding box (OBB) regression. Meanwhile, the adaptive least More >