scholarly journals A 3D Object Detection Based on Multi-Modality Sensors of USV

2019 ◽  
Vol 9 (3) ◽  
pp. 535 ◽  
Author(s):  
Yingying Wu ◽  
Huacheng Qin ◽  
Tao Liu ◽  
Hao Liu ◽  
Zhiqiang Wei
Keyword(s):  
Object Detection ◽  
Marine Environment ◽  
Autonomous Navigation ◽  
3D Object ◽  
Detection Depth ◽  
Generation Network ◽  
Institute Of Technology ◽  
3D Object Detection ◽  
Complicated Surface ◽  
Fusion Detection

Unmanned Surface Vehicles (USVs) are commonly equipped with multi-modality sensors. Fully utilized sensors could improve object detection of USVs. This could further contribute to better autonomous navigation. The purpose of this paper is to solve the problems of 3D object detection of USVs in complicated marine environment. We propose a 3D object detection Depth Neural Network based on multi-modality data of USVs. This model includes a modified Proposal Generation Network and Deep Fusion Detection Network. The Proposal Generation Network improves feature extraction. Meanwhile, the Deep Fusion Detection Network enhances the fusion performance and can achieve more accurate results of object detection. The model was tested on both the KITTI 3D object detection dataset (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and a self-collected offshore dataset. The model shows excellent performance in a small memory condition. The results further prove that the method based on deep learning can give good accuracy in conditions of complicated surface in marine environment.

scholarly journals 3D object detection combining semantic and geometric features from point clouds

Cobot ◽  
2022 ◽  
Vol 1 ◽  
pp. 2
Author(s):  
Hao Peng ◽  
Guofeng Tong ◽  
Zheng Li ◽  
Yaqi Wang ◽  
Yuyuan Shao
Keyword(s):  
Object Detection ◽  
Feature Learning ◽  
Point Clouds ◽  
Difficulty Level ◽  
Geometric Feature ◽  
Geometric Features ◽  
3D Object ◽  
Point Module ◽  
Institute Of Technology ◽  
3D Object Detection

Background: 3D object detection based on point clouds in road scenes has attracted much attention recently. The voxel-based methods voxelize the scene to regular grids, which can be processed with the advanced feature learning frameworks based on convolutional layers for semantic feature learning. The point-based methods can extract the geometric feature of the point due to the coordinate reservations. The combination of the two is effective for 3D object detection. However, the current methods use a voxel-based detection head with anchors for classification and localization. Although the preset anchors cover the entire scene, it is not suitable for detection tasks with larger scenes and multiple categories of objects, due to the limitation of the voxel size. Additionally, the misalignment between the predicted confidence and proposals in the Regions of the Interest (ROI) selection bring obstacles to 3D object detection. Methods: We investigate the combination of voxel-based methods and point-based methods for 3D object detection. Additionally, a voxel-to-point module that captures semantic and geometric features is proposed in the paper. The voxel-to-point module is conducive to the detection of small-size objects and avoids the presets of anchors in the inference stage. Moreover, a confidence adjustment module with the center-boundary-aware confidence attention is proposed to solve the misalignment between the predicted confidence and proposals in the regions of the interest selection. Results: The proposed method has achieved state-of-the-art results for 3D object detection in the  Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) object detection dataset. Actually, as of September 19, 2021, our method ranked 1st in the 3D and Bird Eyes View (BEV) detection of cyclists tagged with difficulty level ‘easy’, and ranked 2nd in the 3D detection of cyclists tagged with ‘moderate’. Conclusions: We propose an end-to-end two-stage 3D object detector with voxel-to-point module and confidence adjustment module.

3D Object Detection Based on Proposal Generation Network Utilizing Monocular Images

2021 ◽  
pp. 1-1
Author(s):  
Qazi Mazhar ul Haq ◽  
Muhamad Amirul Haq ◽  
Shanq-Jang Ruan ◽  
Pei-Jung Liang ◽  
De-Qin Gao
Keyword(s):  
Object Detection ◽  
3D Object ◽  
Generation Network ◽  
3D Object Detection

scholarly journals Scale-Aware Attention-Based PillarsNet (SAPN) Based 3D Object Detection for Point Cloud

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiang Song ◽  
Weiqin Zhan ◽  
Xiaoyu Che ◽  
Huilin Jiang ◽  
Biao Yang
Keyword(s):  
Object Detection ◽  
Autonomous Navigation ◽  
Point Clouds ◽  
Detection Performance ◽  
Detection Methods ◽  
Feature Maps ◽  
3D Object ◽  
3D Point Clouds ◽  
Detection Approach ◽  
3D Object Detection

Three-dimensional object detection can provide precise positions of objects, which can be beneficial to many robotics applications, such as self-driving cars, housekeeping robots, and autonomous navigation. In this work, we focus on accurate object detection in 3D point clouds and propose a new detection pipeline called scale-aware attention-based PillarsNet (SAPN). SAPN is a one-stage 3D object detection approach similar to PointPillar. However, SAPN achieves better performance than PointPillar by introducing the following strategies. First, we extract multiresolution pillar-level features from the point clouds to make the detection approach more scale-aware. Second, a spatial-attention mechanism is used to highlight the object activations in the feature maps, which can improve detection performance. Finally, SE-attention is employed to reweight the features fed into the detection head, which performs 3D object detection in a multitask learning manner. Experiments on the KITTI benchmark show that SAPN achieved similar or better performance compared with several state-of-the-art LiDAR-based 3D detection methods. The ablation study reveals the effectiveness of each proposed strategy. Furthermore, strategies used in this work can be embedded easily into other LiDAR-based 3D detection approaches, which improve their detection performance with slight modifications.

Dense-JANet for Monocular 3D Object Detection

2020 ◽  
Author(s):  
Xiaoqing Shang ◽  
Zhiwei Cheng ◽  
Su Shi ◽  
Zhuanghao Cheng ◽  
Hongcheng Huang
Keyword(s):  
Object Detection ◽  
3D Object ◽  
3D Object Detection

Dense Point Diffusion for 3D Object Detection

2020 ◽  
Author(s):  
Xu Liu ◽  
Jiayan Cao ◽  
Qianqian Bi ◽  
Jian Wang ◽  
Boxin Shi ◽  
...  
Keyword(s):  
Object Detection ◽  
3D Object ◽  
Dense Point ◽  
3D Object Detection

scholarly journals RoIFusion: 3D Object Detection from LiDAR and Vision

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Can Chen ◽  
Luca Zanotti Fragonara ◽  
Antonios Tsourdos
Keyword(s):  
Object Detection ◽  
3D Object ◽  
3D Object Detection
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