Object Detection Using Deep Learning Methods in Traffic Scenarios

2021 ◽  
Vol 54 (2) ◽  
pp. 1-35
Author(s):  
Azzedine Boukerche ◽  
Zhijun Hou
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Driving ◽  
Research Papers ◽  
The Past ◽  
Research Fields ◽  
Lighting Conditions ◽  
Open Research ◽  
Scenario Evaluation ◽  
Traffic Scenario

The recent boom of autonomous driving nowadays has made object detection in traffic scenes a hot topic of research. Designed to classify and locate instances in the image, this is a basic but challenging task in the computer vision field. With its powerful feature extraction abilities, which are vital for object detection, deep learning has expanded its application areas to this field during the past several years and thus achieved breakthroughs. However, even with such powerful approaches, traffic scenarios have their own specific challenges, such as real-time detection, changeable weather, and complex lighting conditions. This survey is dedicated to summarizing research and papers on applying deep learning to the transportation environment in recent years. More than 100 research papers are covered, and different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. Some open research fields are also provided. We believe that it is the first survey focusing on deep learning-based object detection in traffic scenario.

scholarly journals A Survey on Leukemia Detection using Image Processing Techniques

2021 ◽  
Vol 309 ◽  
pp. 01111
Author(s):  
Mohammed Junaid Ahmed ◽  
Padmalaya Nayak
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Research Papers ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Research Fields ◽  
Dataset Size ◽  
Detection And Diagnosis ◽  
Processing Techniques ◽  
Late Works

Leukemia detection and diagnosis by inspecting the blood cell images is an intriguing and dynamic exploration region in both the Artificial Intelligence and Medical research fields. There are numerous procedures created to look at blood tests to identify leukemia illness, these strategies are the customary methods and the deep learning (DL) strategy. This survey paper presents a review on the distinctive conventional strategies and Deep Learning and Machine Learning methods towards that have been utilized in leukemia illness diagnosis dependent on platelets images and to analyze between the two methodologies in nature of appraisal, exactness, cost and speed. This article covers 11 research papers, 9 of these examinations were in customary strategies which utilized image handling and AI (ML) calculations, such as, K-closest neighbor (KNN), K-means, SVM, Naïve Bayes, and 2 investigations in cutting edge procedures which utilized Deep Learning, especially Convolutional Neural Networks (CNNs) which is the most generally utilized in the field leukemia detection since it is profoundly precise, quick, and has the smallest expense. What's more, it dissects various late works that have been presented in the field including the dataset size, the pre-owned procedures, the acquired outcomes, and so forth. At last, in view of the led study, it very well may be reasoned that the proposed framework CNN was accomplishing immense triumphs in the field whether in regards to highlights extraction or classification time, precision and also a best low cost in the identification of leukemia.

Design of robust deep learning-based object detection and classification model for autonomous driving applications

2022 ◽  
Author(s):  
Mesfer Al Duhayyim ◽  
Fahd N. Al-Wesabi ◽  
Anwer Mustafa Hilal ◽  
Manar Ahmed Hamza ◽  
Shalini Goel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Driving ◽  
Classification Model

scholarly journals Deep Learning Approaches for Object Detection

2020 ◽  
pp. 123-145
Author(s):  
Sushma Jaiswal ◽  
Tarun Jaiswal
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Driving ◽  
General Idea ◽  
Detection Methods ◽  
Learning Approaches ◽  
Detection Techniques ◽  
Second Stage ◽  
Fast Pace ◽  
Benchmark Datasets

In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.

scholarly journals A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194228-194239 ◽  
Author(s):  
Yanfen Li ◽  
Hanxiang Wang ◽  
L. Minh Dang ◽  
Tan N. Nguyen ◽  
Dongil Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Driving ◽  
Hybrid Framework ◽  
Detection And Recognition

scholarly journals Multiple Object Detection Based on Clustering and Deep Learning Methods

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4424
Author(s):  
Huu Thu Nguyen ◽  
Eon-Ho Lee ◽  
Chul Hee Bae ◽  
Sejin Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Learning Methods ◽  
Multiple Object ◽  
Multiple Object Detection ◽  
Negative Effect

Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.

scholarly journals Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8381
Author(s):  
Duarte Fernandes ◽  
Tiago Afonso ◽  
Pedro Girão ◽  
Dibet Gonzalez ◽  
António Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Low Cost ◽  
Autonomous Driving ◽  
Test Vehicle ◽  
Real Time Processing ◽  
Cloud Data ◽  
Critical Perception

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform.

scholarly journals Nighttime Reflectance Generation in the Visible Band of Satellites

2019 ◽  
Vol 11 (18) ◽  
pp. 2087 ◽  
Author(s):  
Kimoon Kim ◽  
Ji-Hye Kim ◽  
Yong-Jae Moon ◽  
Eunsu Park ◽  
Gyungin Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
High Temporal Resolution ◽  
Data Sets ◽  
The Past ◽  
Research Fields ◽  
Learning Techniques ◽  
Weather And Climate ◽  
Model Training ◽  
Meteorological Satellite

Visible (VIS) bands, such as the 0.675 μm band in geostationary satellite remote sensing, have played an important role in monitoring and analyzing weather and climate change during the past few decades with coarse spatial and high temporal resolution. Recently, many deep learning techniques have been developed and applied in a variety of applications and research fields. In this study, we developed a deep-learning-based model to generate non-existent nighttime VIS satellite images using the Conditional Generative Adversarial Nets (CGAN) technique. For our CGAN-based model training and validation, we used the daytime image data sets of reflectance in the Communication, Ocean and Meteorological Satellite / Meteorological Imager (COMS/MI) VIS (0.675 μm) band and radiance in the longwave infrared (10.8 μm) band of the COMS/MI sensor over five years (2012 to 2017). Our results show high accuracy (bias = −2.41 and root mean square error (RMSE) = 36.85 during summer, bias = −0.21 and RMSE = 33.02 during winter) and correlation (correlation coefficient (CC) = 0.88 during summer, CC = 0.89 during winter) of values between the observed images and the CGAN-generated images for the COMS VIS band. Consequently, our CGAN-based model can be effectively used in a variety of meteorological applications, such as cloud, fog, and typhoon analyses during daytime and nighttime.

scholarly journals Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7933
Author(s):  
António Silva ◽  
Duarte Fernandes ◽  
Rafael Névoa ◽  
João Monteiro ◽  
Paulo Novais ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
Parallel Architecture ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Resource Limitations ◽  
Hybrid Fpga ◽  
Application Requirements

Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advantage of their capacity to train the networks in reasonable periods and their parallel architecture that allows for high performance and real-time inference. However, these features are limited in autonomous driving due to space, power capacity, and inference time constraints, and onboard devices are not as powerful as their counterparts used for training. This paper investigates the use of a deep learning-based method in edge devices for onboard real-time inference that is power-effective and low in terms of space-constrained demand. A methodology is proposed for deploying high-end GPU-specific models in edge devices for onboard inference, consisting of a two-folder flow: study model hyperparameters’ implications in meeting application requirements; and compression of the network for meeting the board resource limitations. A hybrid FPGA-CPU board is proposed as an effective onboard inference solution by comparing its performance in the KITTI dataset with computer performances. The achieved accuracy is comparable to the PC-based deep learning method with a plus that it is more effective for real-time inference, power limited and space-constrained purposes.

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