scholarly journals A Survey on Monocular 3D Object Detection Algorithms Based on Deep Learning

2020 ◽  
Vol 1518 ◽  
pp. 012049
Author(s):  
Junhui Wu ◽  
Dong Yin ◽  
Jie Chen ◽  
Yusheng Wu ◽  
Huiping Si ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
3D Object ◽  
Detection Algorithms ◽  
3D Object Detection

scholarly journals A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 517
Author(s):  
Seong-heum Kim ◽  
Youngbae Hwang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Low Cost ◽  
Detection Methods ◽  
Future Research ◽  
3D Object ◽  
Practical Applications ◽  
Depth Sensors ◽  
Significant Research ◽  
3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

scholarly journals PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with Point-Based Attentive Cont-Conv Fusion Module

2020 ◽  
Vol 34 (07) ◽  
pp. 12460-12467
Author(s):  
Liang Xie ◽  
Chao Xiang ◽  
Zhengxu Yu ◽  
Guodong Xu ◽  
Zheng Yang ◽  
...  
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Point Clouds ◽  
Semantic Features ◽  
Feature Maps ◽  
3D Object ◽  
Detection Algorithms ◽  
Full Resolution ◽  
Fusion Methods ◽  
3D Object Detection

LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data effectively. However, their fusion methods based on Bird's Eye View (BEV) or voxel format are not accurate. In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points. Except for continuous convolution, we additionally add a Point-Pooling and an Attentive Aggregation to make the fused features more expressive. Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks. PI-RCNN employs a segmentation sub-network to extract full-resolution semantic feature maps from images and then fuses the multi-sensor features via powerful PACF module. Beneficial from the effectiveness of the PACF module and the expressive semantic features from the segmentation module, PI-RCNN can improve much in 3D object detection. We demonstrate the effectiveness of the PACF module and PI-RCNN on the KITTI 3D Detection benchmark, and our method can achieve state-of-the-art on the metric of 3D AP.

scholarly journals Outdoor Mobile Mapping and AI-Based 3D Object Detection with Low-Cost RGB-D Cameras: The Use Case of On-Street Parking Statistics

2021 ◽  
Vol 13 (16) ◽  
pp. 3099
Author(s):  
Stephan Nebiker ◽  
Jonas Meyer ◽  
Stefan Blaser ◽  
Manuela Ammann ◽  
Severin Rhyner
Keyword(s):  
Object Detection ◽  
Smart Cities ◽  
Low Cost ◽  
Point Clouds ◽  
Mobile Mapping ◽  
3D Object ◽  
Depth Measurement ◽  
Detection Algorithms ◽  
3D Point Clouds ◽  
3D Object Detection

A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking.

scholarly journals Grayscale Medical Image Segmentation Method Based on 2D&3D Object Detection with Deep Learning

2021 ◽  
Author(s):  
Yunfei Ge ◽  
Qing Zhang ◽  
Yuantao Sun ◽  
Yidong Shen ◽  
Xijiong Wang
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
3D Object ◽  
Model Driven ◽  
Segmented Object ◽  
3D Object Detection

Abstract Background: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extraction. However, model-driven methods like thresholding usually suffer from wrong segmentation and noises regions because different grayscale images have distinct intensity distribution property thus pre-processing is always demanded. While data-driven methods with deep learning like encoder-decoder networks always are always accompanied by complex architectures which require amounts of training data. Methods: Combining thresholding method and deep learning, this paper presents a novel method by using 2D&3D object detection technologies. First, interest regions contain segmented object are determined with fine-tuning 2D object detection network. Then, pixels in cropped images are turned as point cloud according to their positions and grayscale values. Finally, 3D object detection network is applied to obtain bounding boxes with target points and boxes’ bottoms and tops represent thresholding values for segmentation. After projecting to 2D images, these target points could composite the segmented object. Results: Three groups of grayscale medical images are used to evaluate the proposed image segmentation method. We obtain the IoU (DSC) scores of 0.92 (0.96), 0.88 (0.94) and 0.94 (0.94) for segmentation accuracy on different datasets respectively. Also, compared with five state of the arts and clinically performed well models, our method achieves higher scores and better performance.Conclusions: The prominent segmentation results demonstrate that the built method based on 2D&3D object detection with deep learning is workable and promising for segmentation task of grayscale medical images.

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