scholarly journals HLNet: A Unified Framework for Real-Time Segmentation and Facial Skin Tones Evaluation

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1812
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Real Time ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Skin Tone ◽  
Industrial Applications ◽  
Unified Framework ◽  
Benchmark Datasets ◽  
Low Dimensional ◽  
Color Moment

Real-time semantic segmentation plays a crucial role in industrial applications, such as autonomous driving, the beauty industry, and so on. It is a challenging problem to balance the relationship between speed and segmentation performance. To address such a complex task, this paper introduces an efficient convolutional neural network (CNN) architecture named HLNet for devices with limited resources. Based on high-quality design modules, HLNet better integrates high-dimensional and low-dimensional information while obtaining sufficient receptive fields, which achieves remarkable results on three benchmark datasets. To our knowledge, the accuracy of skin tone classification is usually unsatisfactory due to the influence of external environmental factors such as illumination and background impurities. Therefore, we use HLNet to obtain accurate face regions, and further use color moment algorithm to extract its color features. Specifically, for a 224×224 input, using our HLNet, we achieve 78.39% mean IoU on Figaro1k dataset at over 17 FPS in the case of the CPU environment. We further use the masked color moment for skin tone grade evaluation and approximate 80% classification accuracy demonstrate the feasibility of the proposed method.

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 unified drug–target interaction prediction framework based on knowledge graph and recommendation system

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qing Ye ◽  
Chang-Yu Hsieh ◽  
Ziyi Yang ◽  
Yu Kang ◽  
Jiming Chen ◽  
...  
Keyword(s):  
Drug Target ◽  
Recommendation System ◽  
Drug Repurposing ◽  
Cold Start ◽  
Vital Role ◽  
Knowledge Graph ◽  
Valuable Insight ◽  
Unified Framework ◽  
Benchmark Datasets ◽  
Low Dimensional

AbstractPrediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

scholarly journals Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder–Decoder Shared MLPs with Multiple Losses

2021 ◽  
Vol 13 (16) ◽  
pp. 3121
Author(s):  
Beanbonyka Rim ◽  
Ahyoung Lee ◽  
Min Hong
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Trade Off ◽  
Efficiency And Effectiveness ◽  
Benchmark Datasets ◽  
Scale Characteristics ◽  
3D Lidar ◽  
Geometry Mapping

Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder–decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder–decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing.

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.

scholarly journals Point Cloud Semantic Segmentation with Cross-Correction Features

2022 ◽  
Author(s):  
Yuehua Zhao ◽  
Ma Jie ◽  
Chong Nannan ◽  
Wen Junjie
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Large Scale ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Basic Unit ◽  
Semantic Features ◽  
Point Cloud Segmentation ◽  
Scale Point

Abstract Real time large scale point cloud segmentation is an important but challenging task for practical application like autonomous driving. Existing real time methods have achieved acceptance performance by aggregating local information. However, most of them only exploit local spatial information or local semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial-Semantic Incorporation Network (SSI-Net) for real time large scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High quality contextual features can be learned through SSC by correct and update semantic features using spatial cues, and vice verse. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder-decoder architecture. To ensure efficiency, it also adopts a random sample based hierarchical network structure. Extensive experiments on several prevalent datasets demonstrate that our method can achieve state-of-the-art performance.

A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving

2018 ◽  
Author(s):  
Mennatullah Siam ◽  
Mostafa Gamal ◽  
Moemen Abdel-Razek ◽  
Senthil Yogamani ◽  
Martin Jagersand ◽  
...  
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Semantic Segmentation ◽  
Autonomous Driving

scholarly journals Implementation of a Lightweight Semantic Segmentation Algorithm in Road Obstacle Detection

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7089
Author(s):  
Bushi Liu ◽  
Yongbo Lv ◽  
Yang Gu ◽  
Wanjun Lv
Keyword(s):  
Real Time ◽  
Spatial Information ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Spatial Location ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Depth Information ◽  
Long Time ◽  
Deep Learning Network

Due to deep learning’s accurate cognition of the street environment, the convolutional neural network has achieved dramatic development in the application of street scenes. Considering the needs of autonomous driving and assisted driving, in a general way, computer vision technology is used to find obstacles to avoid collisions, which has made semantic segmentation a research priority in recent years. However, semantic segmentation has been constantly facing new challenges for quite a long time. Complex network depth information, large datasets, real-time requirements, etc., are typical problems that need to be solved urgently in the realization of autonomous driving technology. In order to address these problems, we propose an improved lightweight real-time semantic segmentation network, which is based on an efficient image cascading network (ICNet) architecture, using multi-scale branches and a cascaded feature fusion unit to extract rich multi-level features. In this paper, a spatial information network is designed to transmit more prior knowledge of spatial location and edge information. During the course of the training phase, we append an external loss function to enhance the learning process of the deep learning network system as well. This lightweight network can quickly perceive obstacles and detect roads in the drivable area from images to satisfy autonomous driving characteristics. The proposed model shows substantial performance on the Cityscapes dataset. With the premise of ensuring real-time performance, several sets of experimental comparisons illustrate that SP-ICNet enhances the accuracy of road obstacle detection and provides nearly ideal prediction outputs. Compared to the current popular semantic segmentation network, this study also demonstrates the effectiveness of our lightweight network for road obstacle detection in 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.

scholarly journals REAL-TIME SEMANTIC SLAM WITH DCNN-BASED FEATURE POINT DETECTION, MATCHING AND DENSE POINT CLOUD AGGREGATION

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 399-404
Author(s):  
B. Vishnyakov ◽  
I. Sgibnev ◽  
V. Sheverdin ◽  
A. Sorokin ◽  
P. Masalov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Scene Reconstruction ◽  
Deep Convolutional Neural Networks ◽  
Dense Point ◽  
Robotic Vehicle ◽  
Semantic Scene ◽  
Point Detection

Abstract. In this paper we present the semantic SLAM method based on a bundle of deep convolutional neural networks. It provides real-time dense semantic scene reconstruction for the autonomous driving system of an off-road robotic vehicle. Most state-of-the-art neural networks require large computing resources that go beyond the capabilities of many robotic platforms. We propose an architecture for 3D semantic scene reconstruction on top of the recent progress in computer vision by integrating SuperPoint, SuperGlue, Bi3D, DeepLabV3+, RTM3D and additional module with pre-processing, inference and postprocessing operations performed on GPU. We also updated our simulated dataset for semantic segmentation and added disparity images.

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