scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

scholarly journals SMART CANTEEN : PERILAKU MENGATUR POLA MAKAN DENGAN MEMBACA NILAI NUTRISI (CONVENTIONAL DEEP LEARNING NEURAL NETWORK)

2020 ◽  
Vol 6 (2) ◽  
pp. 115-121
Author(s):  
Ari Purno Wahyu ◽  
Heri Heryono ◽  
Muhammad Benny Chaniago ◽  
Dani Hamdani
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Deep Learning Neural Network

Kesehatan merupakan bagian terpenting bagi kita dimana pengaruh atau datangnya penyakit melalui pola makan, terlebih bagi kita yang memiliki kesibukan yang luar biasa padatnya tentu saja tidak ada waktu untuk sarapan dan lebih memilih makanan cepat saji yang tersedia banyak di kantin atau kafe. Hal ini bukan berarti makanan cepat saji tidak sehat, hal ini akan menjadi masalah jika terlalu berlebih dan tidak memperhatikan takaran saji atau kandungan nutrisi yang ada pada makanan tersebut. Beberapa cara bisa dilakukan dengan menjaga sikap  pola makan misalkan dengan diet atau menggunakan aplikasi perhitungan nutrisi yang ada di pasaran dan gratis untuk diunduh. Jenis aplikasi ini masih kurang efektif dimana aplikasi tersebut masih merupakan perkiraan saja dan tidak bisa digunakan secara realtime. Penelitian sebelumnya bisa menggunakan teknik computer vision dengan menggunakan image sebagai alat pembaca dari makanan yang akan kita santap. Aplikasi tersebut mampu membaca kandungan nutrisi sekaligus  harga makanan, teknik pengolah image yang digunakan menggunakan metode Deep Learning Neural Network, algoritma ini terbukti memiliki akurasi dan pembacaan data yang tinggi dibandingkan algoritma yang lain. Aplikasi dengan Neural Network yang berbasis image bisa diimplementasikan pada mesin kasir di kantin atau cafe dan bisa dibuat dalam bentuk perangkat mobile sehingga lebih mudah digunakan. Teknik komputerisasi dengan Deep Learning Neural Network terbukti bisa diterapkan di kantin dan caf

An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles

2018 ◽  
Author(s):  
Rui Li ◽  
Weitian Wang ◽  
Yi Chen ◽  
Srivatsan Srinivasan ◽  
Venkat N. Krovi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Human Vision ◽  
Automated Driving ◽  
Learning Framework ◽  
Research Platform ◽  
Automatic Parking ◽  
Fully Automatic

Fully automatic parking (FAP) is a key step towards the age of autonomous vehicle. Motivated by the contribution of human vision to human parking, in this paper, we propose a computer vision based FAP method for the autonomous vehicles. Based on the input images from a rear camera on the vehicle, a convolutional neural network (CNN) is trained to automatically output the steering and velocity commands for the vehicle controlling. The CNN is trained by Caffe deep learning framework. A 1/10th autonomous vehicle research platform (1/10-SAVRP), which configured with a vehicle controller unit, an automated driving processor, and a rear camera, is used for demonstrating the parking maneuver. The experimental results suggested that the proposed approach enabled the vehicle to gain the ability of parking independently without human input in different driving settings.

scholarly journals Detecting Vehicle Motion using Deep Gaussian Mixture Model with SCI-kit Learn

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1216-1218
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Mixture Models ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Vehicle Motion ◽  
Deep Learning Neural Network ◽  
Architecture Development

Detecting vehicle motions are a progressively significant part in road surveillance and Traffic organizing systems. This paper presents a new Deep Gaussian based mixture model that predicts accurate in detecting vehicle motions. Although the existing arrangements based on conventional Gaussian mixture model which is limited in insufficient of many distinct points which eliminate covariance and solutions relative to infinite likelihood. In the proposed scheme, the deep learning neural network is used for including the more points with nested mixture models. To overcome the effects of adding more points the modification achieved in architecture development. The validation of proposed scheme is achieved with real-time videos and process with scikit learn based model.

scholarly journals Differentiating Birds and Animals using Deep Learning Neural Network with Image Processing Approach

2020 ◽  
Vol 5 (9) ◽  
pp. 609-613
Author(s):  
K Pandiaraj ◽  
P Sivakumar ◽  
V Nandhini ◽  
S Parkav
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
The Real ◽  
Processing Approach ◽  
Human Effort ◽  
Deep Learning Neural Network

In farms we can see that the birds and animals destroying the crops. The movement of birds and animals cannot be controlled by any method. We can only drive away them. To drive away them, humans are used. To reduce the human effort we have introduced a method using image processing. In this method, the real time images are given as input and sound will be derived as output. The image given as input is compared with the trained images and classified into birds and animals. After the identification, birds can be driven away by using cracker sound and animals can be driven away by using a human sound.

scholarly journals Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7267
Author(s):  
Luiz G. Galvao ◽  
Maysam Abbod ◽  
Tatiana Kalganova ◽  
Vasile Palade ◽  
Md Nazmul Huda
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Detection Performance ◽  
The Past ◽  
Perception System ◽  
Good Detection

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

scholarly journals Hybrid Multi-Sensor Integration for Static or Dynamic Obstacle Detection, Tracking and Classification for Autonomous Vehicle

2021 ◽  
Vol 23 (06) ◽  
pp. 1288-1293
Author(s):  
Dr. S. Rajkumar ◽  
◽  
Aklilu Teklemariam ◽  
Addisalem Mekonnen ◽  
◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Multiple Sensors ◽  
Environment Perception ◽  
Hybrid Sensors ◽  
The Individual

Autonomous Vehicles (AV) reduces human intervention by perceiving the vehicle’s location with respect to the environment. In this regard, utilization of multiple sensors corresponding to various features of environment perception yields not only detection but also enables tracking and classification of the object leading to high security and reliability. Therefore, we propose to deploy hybrid multi-sensors such as Radar, LiDAR, and camera sensors. However, the data acquired with these hybrid sensors overlaps with the wide viewing angles of the individual sensors, and hence convolutional neural network and Kalman Filter (KF) based data fusion framework was implemented with a goal to facilitate a robust object detection system to avoid collisions inroads. The complete system tested over 1000 road scenarios for real-time environment perception showed that our hardware and software configurations outperformed numerous other conventional systems. Hence, this system could potentially find its application in object detection, tracking, and classification in a real-time environment.

scholarly journals Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4357 ◽  
Author(s):  
Babak Shahian Jahromi ◽  
Theja Tulabandhula ◽  
Sabri Cetin
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Sensor Data ◽  
Detection Accuracy ◽  
System Solution ◽  
Environment Perception ◽  
Fusion Framework

There are many sensor fusion frameworks proposed in the literature using different sensors and fusion methods combinations and configurations. More focus has been on improving the accuracy performance; however, the implementation feasibility of these frameworks in an autonomous vehicle is less explored. Some fusion architectures can perform very well in lab conditions using powerful computational resources; however, in real-world applications, they cannot be implemented in an embedded edge computer due to their high cost and computational need. We propose a new hybrid multi-sensor fusion pipeline configuration that performs environment perception for autonomous vehicles such as road segmentation, obstacle detection, and tracking. This fusion framework uses a proposed encoder-decoder based Fully Convolutional Neural Network (FCNx) and a traditional Extended Kalman Filter (EKF) nonlinear state estimator method. It also uses a configuration of camera, LiDAR, and radar sensors that are best suited for each fusion method. The goal of this hybrid framework is to provide a cost-effective, lightweight, modular, and robust (in case of a sensor failure) fusion system solution. It uses FCNx algorithm that improve road detection accuracy compared to benchmark models while maintaining real-time efficiency that can be used in an autonomous vehicle embedded computer. Tested on over 3K road scenes, our fusion algorithm shows better performance in various environment scenarios compared to baseline benchmark networks. Moreover, the algorithm is implemented in a vehicle and tested using actual sensor data collected from a vehicle, performing real-time environment perception.

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