scholarly journals Redes neurais convolucionais aplicas à detecção de objetos no domínio de futebol de robôs humanoides

2021 ◽  
Author(s):  
◽  
Lucas Ribeiro de Abreu
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Critical System ◽  
Test Set ◽  
Learning Techniques ◽  
Using Data ◽  
Vision Module

The RoboCup Soccer is one of the largest initiatives in the robotics field of research. This initiative considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. This work evaluates deep neural networks for the detection of the ball and robots. For such task, five convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an mAP of 0.98 and 14.7 frames per second, running on CPU

scholarly journals Real-time Ball Detection for Robocup Soccer Using Convolutional Neural Networks

2019 ◽  
Author(s):  
Lucas Ribeiro De Abreu ◽  
Reinaldo Augusto da Costa Bianchi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Average Precision ◽  
Critical System ◽  
Soccer Ball ◽  
Learning Techniques ◽  
Using Data ◽  
Vision Module ◽  
Ball Detection

The RoboCup Soccer is one of the largest competitions in the robotics field of research. It considers the soccer match as a challenge for the robots and aims to win a match between humans versus robots by the year of 2050. The vision module is a critical system for the robots because it needs to quickly locate and classify objects of interest for the robot in order to generate the next best action. In this paper, an approach using Convolutional Neural Networks for object detection is described. The soccer ball is the chosen object and three state-ofart convolutional neural networks architectures were trained for the experiment using data augmentation and transfer learning techniques. The models were evaluated in a test set, yielding promising results in precision and frames per second. The best model achieved an average precision of 0.972 with an intersection over union of 50% and 9.64 frames per second, running on CPU.

scholarly journals The Use of Synthetic Data to Facilitate Eye Segmentation Using Deeplabv3+

2021 ◽  
Vol 5 (3) ◽  
pp. 1-10
Author(s):  
Melih Öz ◽  
Taner Danışman ◽  
Melih Günay ◽  
Esra Zekiye Şanal ◽  
Özgür Duman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Real Life ◽  
Synthetic Data ◽  
Limited Data ◽  
Test Set ◽  
Human Eye ◽  
Context Data ◽  
Head Positions

The human eye contains valuable information about an individual’s identity and health. Therefore, segmenting the eye into distinct regions is an essential step towards gathering this useful information precisely. The main challenges in segmenting the human eye include low light conditions, reflections on the eye, variations in the eyelid, and head positions that make an eye image hard to segment. For this reason, there is a need for deep neural networks, which are preferred due to their success in segmentation problems. However, deep neural networks need a large amount of manually annotated data to be trained. Manual annotation is a labor-intensive task, and to tackle this problem, we used data augmentation methods to improve synthetic data. In this paper, we detail the exploration of the scenario, which, with limited data, whether performance can be enhanced using similar context data with image augmentation methods. Our training and test set consists of 3D synthetic eye images generated from the UnityEyes application and manually annotated real-life eye images, respectively. We examined the effect of using synthetic eye images with the Deeplabv3+ network in different conditions using image augmentation methods on the synthetic data. According to our experiments, the network trained with processed synthetic images beside real-life images produced better mIoU results than the network, which only trained with real-life images in the Base dataset. We also observed mIoU increase in the test set we created from MICHE II competition images.

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

scholarly journals Transfer Incremental Learning Using Data Augmentation

2018 ◽  
Vol 8 (12) ◽  
pp. 2512 ◽  
Author(s):  
Ghouthi Boukli Hacene ◽  
Vincent Gripon ◽  
Nicolas Farrugia ◽  
Matthieu Arzel ◽  
Michel Jezequel
Keyword(s):  
Incremental Learning ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Complexity ◽  
Computational Power ◽  
Learning Problem ◽  
Learning Techniques ◽  
Using Data ◽  
Selection Of

Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which consists of learning incrementally new classes and examples over time. Combining the outstanding performances of Deep Neural Networks (DNNs) with the flexibility of incremental learning techniques is a promising venue of research. In this contribution, we introduce Transfer Incremental Learning using Data Augmentation (TILDA). TILDA is based on pre-trained DNNs as feature extractors, robust selection of feature vectors in subspaces using a nearest-class-mean based technique, majority votes and data augmentation at both the training and the prediction stages. Experiments on challenging vision datasets demonstrate the ability of the proposed method for low complexity incremental learning, while achieving significantly better accuracy than existing incremental counterparts.

scholarly journals Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Alejandro Baldominos ◽  
Yago Saez ◽  
Pedro Isasi
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Deep Neural Networks ◽  
Handwriting Recognition ◽  
Computational Time ◽  
Learning Models ◽  
Expensive Process

Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we take advantage of the fact that evolutionary algorithms optimize a population of candidate solutions, by combining a set of the best evolved models resulting in a committee of convolutional neural networks. This process is enhanced by using specific mechanisms to preserve the diversity of the population. Additionally, in this paper, we address one of the disadvantages of neuroevolution: the process is very expensive in terms of computational time. To lessen this issue, we explore the performance of topology transfer learning: whether the best topology obtained using neuroevolution for a certain domain can be successfully applied to a different domain. By doing so, the expensive process of neuroevolution can be reused to tackle different problems, turning it into a more appealing approach for optimizing the design of neural networks topologies. After evaluating our proposal, results show that both the use of neuroevolved committees and the application of topology transfer learning are successful: committees of convolutional neural networks are able to improve classification results when compared to single models, and topologies learned for one problem can be reused for a different problem and data with a good performance. Additionally, both approaches can be combined by building committees of transferred topologies, and this combination attains results that combine the best of both approaches.

scholarly journals Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Lucas Wayne Welch ◽  
Xudong Liu ◽  
Indika Kahanda ◽  
Sandeep Reddivari ◽  
Karthikeyan Umapathy
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Vegetation Coverage ◽  
Vegetation Monitoring ◽  
Test Set ◽  
Human Labor ◽  
National Estuarine Research Reserve ◽  
Multiple Species ◽  
Marsh Grass

Vegetation monitoring is one of the major cornerstones of environmental protection today, giving scientists a look into changing ecosystems. One important task in vegetation monitoring is to estimate the coverage of vegetation in an area of marsh. This task often calls for extensive human labor carefully examining pixels in photos of marsh sites, a very time-consuming process. In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. Then, we focus on the utmost component to build convolutional neural networks (CNNs) to identify the presence or absence of vegetation. To this end, we collect a new dataset with the help of Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) to be used to train and test the effectiveness of our selected CNN models, including LeNet-5 and two variants of AlexNet. Our experiments show that the AlexNet variants achieves higher accuracy scores on the test set than LeNet-5, with 92.41\% for a AlexNet variant ondistinguishing between vegetation and the lack thereof. These promising results suggest us to confidently move forward with not only expanding our dataset, but also developing models to determine multiple species in addition to the presence of live vegetation.

Pedestrian-Motorcycle Binary Classification Using Data Augmentation and Convolutional Neural Networks

2019 ◽  
pp. 725-737
Author(s):  
Robert Kerwin C. Billones ◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim ◽  
Edwin Sybingco ◽  
Alexis M. Fillone ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Binary Classification ◽  
Using Data
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