scholarly journals Real time deep learning framework to monitor social distancing using improved single shot detector based on overhead position

2022 ◽  
Author(s):  
Bharathi Gopal ◽  
Anandharaj Ganesan
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Single Shot ◽  
Social Distancing ◽  
Learning Framework

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals Sistem Pendeteksi Pejalan Kaki di Lingkungan Terbatas Berbasis SSD Mobilenetv1 Menggunakan Gambar 360° Ternormalisasi

2021 ◽  
Vol 3 ◽  
pp. 111-122
Author(s):  
Ni Nyoman Ayu Marlina ◽  
Denden Mohammad Ariffin ◽  
Arief Suryadi Satyawan ◽  
Mohammed Ikrom Asysyakuur ◽  
Muhammad Farhan Utamajaya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Single Shot

Seiring dengan perkembangan zaman, setiap produsen mobil selalu menciptakan produkterbarunya lebih canggih. Ide ini kemudian melahirkan konsep kendaraan listrik otonom (KLO). Hal ini dimaksudkan untuk selalu menghadirkan kendaraan yang dapat memenuhi selera konsumen yang terus berkembang, disamping juga ramah lingkungan Kehadiran kendaraan listrik otonom pastinya akan dialami oleh Indonesia yang masyarakatnya sudah mulai bergantung pada alat transportasi mobil. Oleh sebab itu situasi ini mengharuskan kita bersiap menghadapi era Mobility in Society 5.0, dimana kita harus dapat menguasai teknologi pendukungnya. Kendaraan litrik otonom dapat terealisasi jika sistemnya mampu mendeteksi objek dengan baik. Oleh sebab itu pada penelitian ini dilakukan pengembangan sistem pendeteksi pejalan kaki berbasis deep learning dan memanfaatkan gambar 360°. Sistem software deteksi objek yang dibangun menggunakan Single Shot Multibox Detector (SSD) MobilenetV1, sedangkan hardware yang digunakan untuk pengembangan ini adalah Jetson AGX Xavier. Proses pengembangan yang dilakukan dimulai dari pengambilan gambar 360° ternormalisasi berisi informasi pejalan kaki di area kampus Universitas Nurtanio yang dipergunakan sebagai dataset dan data pengujian, melatih SSD MobileNetV1 dengan dataset tersebut (19.038), dan menguji model software terlatih secara real-time maupun offline.Hasil pengujian offline terhadap 735 gambar 360° pada kondisi siang hari menunjukan bahwa55,5% gambar dapat terdeteksi sempurna, sedangkan dari 595 gambar 360° pada kondisi sore hari, 51,2% gambar dapat terdeteksi sempurna. Pada pengujian secara real-time diperoleh kepastian bahwa 98% pejalan kaki pada siang hari terdeteksi, sedangkan pada sore hari hanya 95%. Waktu proses rata-rata pada sebuah gambar kondisi siang hari adalah 32,81283 ms jika menggunakan CPU, sedangkanjika menggunakan GPU adalah 32,79766 ms. Untuk sebuah gambar dengan informasi yang sama pada kondisi sore hari diperoleh waktu proses 37,42598 ms jika menggunakan CPU, sedangkan jika menggunakan GPU adalah 37,45174 ms.

scholarly journals Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

2021 ◽  
Vol 5 ◽  
pp. 182-196
Author(s):  
Muhammad Haris Kaka Khel ◽  
Kushsairy Kadir ◽  
Waleed Albattah ◽  
Sheroz Khan ◽  
MNMM Noor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Social Distance ◽  
Virus Transmission ◽  
World Health ◽  
Single Shot ◽  
Accuracy Score ◽  
Computationally Efficient ◽  
Safe Distance ◽  
Crowd Management

Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF

scholarly journals Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1932
Author(s):  
Malik Haris ◽  
Adam Glowacz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Single Shot ◽  
Automated Driving ◽  
Convolutional Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

scholarly journals Ship Detection Based on YOLOv2 for SAR Imagery

2019 ◽  
Vol 11 (7) ◽  
pp. 786 ◽  
Author(s):  
Yang-Lang Chang ◽  
Amare Anagaw ◽  
Lena Chang ◽  
Yi Wang ◽  
Chih-Yu Hsiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Experimental Results ◽  
Detection Methods ◽  
Computational Time ◽  
Detection Accuracy ◽  
Single Shot ◽  
Ship Detection ◽  
Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

scholarly journals Real‐time comprehensive glass container inspection system based on deep learning framework

2019 ◽  
Vol 55 (3) ◽  
pp. 131-132 ◽  
Author(s):  
Qiaokang Liang ◽  
Shao Xiang ◽  
Jianyong Long ◽  
Wei Sun ◽  
Yaonan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Glass Container ◽  
Inspection System ◽  
Learning Framework ◽  
Container Inspection
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