Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network

Rahmat, Romi Fadillah; Dennis, Dennis; Sitompul, Opim Salim; Purnamawati, Sarah; Budiarto, Rahmat

doi:10.12928/telkomnika.v17i5.11276

Cited by 19 publications

(8 citation statements)

References 19 publications

(16 reference statements)

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“…Although the black and white face images are robust in face recognition but they are excluded, The VGGFace2 dataset focused on facial and image variation due to color processing as shown in Figure 4. Five age classes have been included in this study {(00-10), (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), (36-54), (55-90)}.…”

Section: Databasementioning

confidence: 99%

“…Before loading input images, the working environment need some preparation such as installing the required libraries, then start to load the dataset. A preprocessing data, is required at this stage including cleansing the dataset from low quality images which will confuse the training model, A relabeling age group images to five categories {(00-10), (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), (36-54), (55-90)}, splitting dataset to 80% train, 10% test and 10% validation and finally, set the batch size. The age estimation model is trained with three pre-trained-weight, and two deep learning algorithms (VGG-Face and ResNet50) as it illustrated in the experimental work.…”

Section: Preprocessing Datasetmentioning

confidence: 99%

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Pedestrian age estimation based on deep learning

Abdullah

Ghazal²,

Waisi³

2021

IJEECS

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The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.

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Section: Databasementioning

confidence: 99%

Section: Preprocessing Datasetmentioning

confidence: 99%

Pedestrian age estimation based on deep learning

Abdullah

Ghazal²,

Waisi³

2021

IJEECS

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“…Convolutional neural networks (CNNs) applied on the dataset of image data (especially lung X-ray) [3] for classification of pneumonia disease and the result was obtained an accuracy rate of 97%. The AlexNet's deep convolutional neural network used as a pre-trained neural network with 1000 categories for image classification [6] to detect and geotag advertisement billboard in real-time condition, and experimental results achieved 92.7% training accuracy for advertisement billboard detection. By using convolutional neural networks, Z. Rustam, et al, [7] proposed the method to assist doctors in providing the appropriate beliefs and predictions to patients, the results showed the capability of CNNs method to accurately identify the patient's X-ray test images.…”

Section: Introductionmentioning

confidence: 99%

Applying convolutional neural networks for limited-memory application

et al. 2020

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Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.

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“…Based on the test result, obtained accuracy is 98,2% for detecting vehicle objects such as bus, car, and motorcycle. The other research which implemented deep learning to identify object was research about advertisement billboard detection [5]. This research uses AlexNet Deep Convolutional Neural Network (DCNN) method.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Object Detection For Wayang Punakawan Identification Using Deep Learning

Thohari¹,

Adhitama²

2019

INFOTEL

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Indonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately.

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Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network

Cited by 19 publications

References 19 publications

Pedestrian age estimation based on deep learning

Pedestrian age estimation based on deep learning

Applying convolutional neural networks for limited-memory application

Real-Time Object Detection For Wayang Punakawan Identification Using Deep Learning

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