Investigasi pengaruh Step Training pada Skema Same-Padding untuk Metode Faster R-CNN dalam Teknologi Augmented Reality

Abstract Augmented Reality (AR) is a technology with the concept of combining real-world dimensions with virtual world dimensions that are displayed in realtime. In the AR environment, interaction techniques used can vary. Marker-based AR is one type of AR that allows virtual objects to be displayed in the real world by using markers as pointers. In the use of marker-based AR required object detection method used for tracking markers. In this study, a system that can detect objects in the form of fingertips will be designed. In designing the system the Faster Region-based Convolutional Neural Network (Faster R-CNN) method is used. R-CNN Faster is an object detection method which is a combination of the Fast R-CNN method and the Region Proposal Network (RPN). The results of the detection parameters will be used for tracking, namely the coordinates x, y, width, and length. This research uses the Faster R-CNN method because it has a faster computing speed compared to the previous method, namely Particle Filter. The Faster R-CNN method uses ResNet architecture as the core of CNN. The system configuration to be tested is the 25K, 50K and 75K step training with the same-padding scheme. The testing process is taken from a video consisting of 10800 training data and 3600 test data. The best system configuration based on parameter priority for AR technology is obtained in the 50K step training.Keyword: augmented reality, convolutional neural network, faster region-based convolutional neural network, region proposal network, ResNet.Abstrak Augmented Reality (AR) adalah teknologi dengan konsep menggabungkan dimensi dunia nyata dengan dimensi dunia virtual yang ditampilkan secara real-time. Dalam lingkungan AR, teknik interaksi yang digunakan dapat bermacam – macam. Marker-based AR merupakan salah satu jenis AR yang memungkinkan objek virtual ditampilkan ke dalam dunia nyata dengan digunakannya marker sebagai pointer-nya. Dalam penggunaan AR berbasis marker diperlukan metode deteksi objek yang digunakan untuk tracking marker. Dalam penelitian ini akan dirancang sebuah sistem yang dapat mendeteksi objek berupa ujung jari. Dalam perancangan sistem tersebut digunakan metode Faster Region-Based Convolutional Nueral Network (Faster R-CNN). Faster R-CNN merupakan salah satu metode deteksi objek yang merupakan gabungan dari metode Fast R-CNN dan Region Proposal Network (RPN). Hasil dari parameter deteksi akan digunakan untuk tracking, yaitu koordinat x, y, width, dan length. Penelitian ini menggunakan metode Faster R-CNN karena memiliki kecepatan komputasi yang lebih cepat dibandingkan dengan metode sebelumnya yaitu Particle Filter. Metode Faster R-CNN mengunakan arsitektur ResNet sebagai inti dari CNN. Konfigurasi sistem yang akan diuji adalah step training 25K, 50K dan 75K dengan skema same-padding. Proses pengujian diambil dari video yang terdiri dari 10800 data latih dan 3600 data uji. Konfigurasi sistem terbaik berdasarkan prioritas parameter untuk teknologi AR didapatkan pada step training 50K.Keyword: augmented reality, convolutional neural network, faster region-based convolutional neural network, region proposal network, ResNet.

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Abandoned Object Detection Method Using Convolutional Neural Network

2020 International Conference on ICT for Smart Society (ICISS) ◽

10.1109/iciss50791.2020.9307547 ◽

2020 ◽

Author(s):

Saluky Saluky ◽

Suhono Harso Supangkat ◽

IGB Baskara Nugraha

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Method ◽

Abandoned Object

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End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy

Soft Matter ◽

10.1039/c9sm01979k ◽

2020 ◽

Vol 16 (7) ◽

pp. 1751-1759 ◽

Cited By ~ 2

Author(s):

Eric N. Minor ◽

Stian D. Howard ◽

Adam A. S. Green ◽

Matthew A. Glaser ◽

Cheol S. Park ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Experimental Data ◽

Object Detection ◽

Convolutional Neural Network ◽

Real World ◽

Simulated Data ◽

Video Microscopy ◽

Experimental Physics ◽

Simulated Images

We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data.

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A Multibranch Object Detection Method for Traffic Scenes

Computational Intelligence and Neuroscience ◽

10.1155/2019/3679203 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Jiangfan Feng ◽

Fanjie Wang ◽

Siqin Feng ◽

Yongrong Peng

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Method ◽

State Of The Art ◽

Recall Rate ◽

Small Scale ◽

Feature Maps ◽

Time Requirements ◽

Speed Up

The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16 × 16, 32 × 32, and 64 × 64 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33 fps) is fast, which can meet the real-time requirements of industry.

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A novel directional object detection method for piled objects using a hybrid region-based convolutional neural network

Advanced Engineering Informatics ◽

10.1016/j.aei.2021.101448 ◽

2022 ◽

Vol 51 ◽

pp. 101448

Author(s):

Ming-Chuan Chiu ◽

Ho-Yen Tsai ◽

Jing-Er Chiu

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Method ◽

Hybrid Region

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Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network

PLoS ONE ◽

10.1371/journal.pone.0256290 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256290

Author(s):

Taehan Koo ◽

Moon Hwan Kim ◽

Mihn-Sook Jue

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Sensitivity And Specificity ◽

Real World ◽

Data Model ◽

Fungal Infections ◽

Curve Analysis ◽

Microscopic Images

Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer’s skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.

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Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality

Jurnal Ilmiah FIFO ◽

10.22441/fifo.2020.v12i1.001 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1

Author(s):

Vivian Alfionita Sutama ◽

Suryo Adhi Wibowo ◽

Rissa Rahmania

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Augmented Reality ◽

Object Detection ◽

Convolutional Neural Network ◽

Real Time ◽

Transfer Learning ◽

Learning Rate ◽

Batch Size ◽

Single Shot

Nowadays, Artificial Intelligence is one of the most developing technology, especially on Augmented Reality (AR). AR is a technology which connected between real world and virtual in a real time that allows user to interact directly and display it in 3D. AR technology has two methods, that are AR based on marker and AR based on markerless. However, AR based on marker need an object detection system which has high performance as an interaction tools between user and the device. Single shot multibox detector (SSD) is an object detection algorithm that has fast learning computation and good performance. This method is affected by some parameters like number of epoch, learning rate, batch size, step training, etc. However, to create a good system it took a long process such as taking dataset, labelling process, then training and testing models to gain the best performance. In this experiment, we analyze SSD method in AR technology using inception architecture as pre-trained Convolutional neural network (CNN), and then do transfer learning to minimize amount training time. The configuration that used is the number of step training. The result of this experiment gets the best accuracy in 70.17%. Then, the best performance is used as an object detection model for marker’s AR technology.Abstrak Saat ini, Artificial intelligence merupakan teknologi yang sedang berkembang pesat. Salah satunya adalah teknologi Augmented Reality (AR). AR adalah teknologi yang menggabungkan dunia nyata dengan virtual secara real-time dengan interaksi pengguna secara langsung dan menampilkannya dalam bentuk 3D. Teknologi AR ini memiliki dua metode yaitu dengan marker dan markerless. Dalam perkembangannya, AR berbasis marker membutuhkan sistem deteksi objek yang memiliki performa tinggi sebagai alat interaksi antara pengguna dengan perangkatnya. Single shot multibox detector (SSD) merupakan algoritma deteksi objek yang memiliki komputasi pembelajaran dan kinerja yang baik. Metode ini dipengaruhi oleh beberapa parameter seperti jumlah lapisan konvolusi, epoch, learning rate, jumlah batch, step training, dll. Namun, dalam mengimplementasikannya diperlukan proses yang cukup panjang seperti, pengambilan dataset, proses pelabelan, proses pelatihan menggunakan metode SSD, dan melakukan pengujian terhadap beberapa model untuk mencari perfomansi paling baik. Dalam percobaan ini, kami melakukan analisis terhadap metode SSD pada teknologi AR menggunakan arsitektur Inception sebagai pre-trained Convolutional neural network (CNN), kemudian dilakukan transfer learning untuk memperkecil jumlah kelas data pelatihan dan waktu pelatihan data. Konfigurasi yang digunakan berupa jumlah step pada pelatihan. Hasil dari penilitian ini menunjukan akurasi terbaik sebesar 70,17%. Kemudian, perfomansi terbaik digunakan sebagai model deteksi objek untuk marker pada teknologi AR.

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Research on Vehicle Object Detection Method Based on Convolutional Neural Network

2018 11th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid.2018.00068 ◽

2018 ◽

Cited By ~ 2

Author(s):

Qinghui Zhang ◽

Chenxia Wan ◽

Shanfeng Bian

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Method

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Research on Optimization of Object Detection Technology Based on Convolutional Neural Network

2020 13th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid51228.2020.00010 ◽

2020 ◽

Author(s):

Yang Xue ◽

Huang Wanjun ◽

Yu Hongyang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Technology

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Geometric property-based convolutional neural network for indoor object detection

International Journal of Advanced Robotic Systems ◽

10.1177/1729881421993323 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142199332

Author(s):

Xintao Ding ◽

Boquan Li ◽

Jinbao Wang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Geometric Property ◽

Ground Truth ◽

Geometric Constraints ◽

Depth Information ◽

Training Set ◽

Object Knowledge ◽

The Mean

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

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