Real-time object detection and semantic segmentation for autonomous driving

2018 ◽  
Author(s):  
Weichao Xu ◽  
Baojun Li ◽  
Sun Liu ◽  
Wei Qiu
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Semantic Segmentation ◽  
Autonomous Driving

scholarly journals DNS: A multi-scale deconvolution semantic segmentation network for joint detection and segmentation

2019 ◽  
Vol 277 ◽  
pp. 02005
Author(s):  
Ning Feng ◽  
Le Dong ◽  
Qianni Zhang ◽  
Ning Zhang ◽  
Xi Wu ◽  
...  
Keyword(s):  
Image Analysis ◽  
Object Detection ◽  
Real Time ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Joint Detection ◽  
Multi Scale ◽  
Segmentation Task

Real-time semantic segmentation has become crucial in many applications such as medical image analysis and autonomous driving. In this paper, we introduce a single semantic segmentation network, called DNS, for joint object detection and segmentation task. We take advantage of multi-scale deconvolution mechanism to perform real time computations. To this goal, down-scale and up-scale streams are utilized to combine the multi-scale features for the final detection and segmentation task. By using the proposed DNS, not only the tradeoff between accuracy and cost but also the balance of detection and segmentation performance are settled. Experimental results for PASCAL VOC datasets show competitive performance for joint object detection and segmentation task.

A RGB-D Based Real-Time Multiple Object Detection and Ranging System for Autonomous Driving

2020 ◽  
Vol 20 (20) ◽  
pp. 11959-11966
Author(s):  
Jiachen Yang ◽  
Chenguang Wang ◽  
Huihui Wang ◽  
Qiang Li
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Driving ◽  
Multiple Object ◽  
Multiple Object Detection

scholarly journals Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps

2019 ◽  
pp. 567-582 ◽  
Author(s):  
B. Ravi Kiran ◽  
Luis Roldão ◽  
Beñat Irastorza ◽  
Renzo Verastegui ◽  
Sebastian Süss ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Driving ◽  
Dynamic Object ◽  
Time Dynamic

scholarly journals Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

2020 ◽  
pp. 1-20 ◽  
Author(s):  
Di Feng ◽  
Christian Haase-Schutz ◽  
Lars Rosenbaum ◽  
Heinz Hertlein ◽  
Claudius Glaser ◽  
...  
Keyword(s):  
Object Detection ◽  
Semantic Segmentation ◽  
Autonomous Driving

scholarly journals Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8072
Author(s):  
Yu-Bang Chang ◽  
Chieh Tsai ◽  
Chang-Hong Lin ◽  
Poki Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Trade Off ◽  
Segmentation Methods ◽  
General Semantic ◽  
Deep Learning Model

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission.

scholarly journals A New Dataset and Performance Evaluation of a Region-Based CNN for Urban Object Detection

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 301 ◽  
Author(s):  
Alex Dominguez-Sanchez ◽  
Miguel Cazorla ◽  
Sergio Orts-Escolano
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Driving ◽  
Learning Techniques ◽  
Driving Assistance ◽  
Large Growth ◽  
Weakly Supervised ◽  
And Performance ◽  
Detection And Localization ◽  
Baseline Approach

In recent years, we have seen a large growth in the number of applications which use deep learning-based object detectors. Autonomous driving assistance systems (ADAS) are one of the areas where they have the most impact. This work presents a novel study evaluating a state-of-the-art technique for urban object detection and localization. In particular, we investigated the performance of the Faster R-CNN method to detect and localize urban objects in a variety of outdoor urban videos involving pedestrians, cars, bicycles and other objects moving in the scene (urban driving). We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted on a vehicle. Furthermore, some of the data is weakly annotated so it can be used for testing weakly supervised learning techniques. There already exist urban object datasets, but none of them include all the essential urban objects. We carried out extensive experiments demonstrating the effectiveness of the baseline approach. Additionally, we propose an R-CNN plus tracking technique to accelerate the process of real-time urban object detection.

scholarly journals Real-Time LiDAR Point Cloud Semantic Segmentation for Autonomous Driving

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Xing Xie ◽  
Lin Bai ◽  
Xinming Huang
Keyword(s):  
Real Time ◽  
Power Efficiency ◽  
Point Cloud ◽  
Processing Time ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Geometric Information ◽  
Embedded Platform ◽  
Gpu Implementation

LiDAR has been widely used in autonomous driving systems to provide high-precision 3D geometric information about the vehicle’s surroundings for perception, localization, and path planning. LiDAR-based point cloud semantic segmentation is an important task with a critical real-time requirement. However, most of the existing convolutional neural network (CNN) models for 3D point cloud semantic segmentation are very complex and can hardly be processed at real-time on an embedded platform. In this study, a lightweight CNN structure was proposed for projection-based LiDAR point cloud semantic segmentation with only 1.9 M parameters that gave an 87% reduction comparing to the state-of-the-art networks. When evaluated on a GPU, the processing time was 38.5 ms per frame, and it achieved a 47.9% mIoU score on Semantic-KITTI dataset. In addition, the proposed CNN is targeted on an FPGA using an NVDLA architecture, which results in a 2.74x speedup over the GPU implementation with a 46 times improvement in terms of power efficiency.

Sign in / Sign up

Export Citation Format

Share Document