MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models

İni̇k, Özkan; Altıok, Mustafa; Ülker, Erkan; Koçer, Barış

doi:10.1016/j.asoc.2021.107582

Cited by 24 publications

(11 citation statements)

References 40 publications

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“…4 CNN is a good DL method in image processing, which helps to extract the features of the image well. Its core method is to use convolution to verify the number of images and then filter the image to extract from each face and direction of the image to ensure that many features can be obtained [32,33]. Many features can be quickly extracted from the image only by training fewer convolution parameters.…”

Section: B Functional Modules Of the Traffic Iasmentioning

confidence: 99%

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Han

2022

IEEE Access

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In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model's accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future INDEX TERMS deep learning; Internet of Things; signal acquisition; system design; vehicle identification and positioning

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Section: B Functional Modules Of the Traffic Iasmentioning

confidence: 99%

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Han

2022

IEEE Access

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“…There are various ways to describe datasets, such as attributes used to define types of things, both qualitative and quantitative. This research uses a public dataset, which https://doi.org/10.24036/tip.v15i1 5 P.ISSN: 2086 -4981 E.ISSN: 2620 -6390 tip.ppj.unp.ac.id means using a dataset from a public repository that has been approved by previous researchers and can be accessed globally [15].…”

Section: System Implementation 321 Data Setmentioning

confidence: 99%

“…CNN typically leverages the convolution method by applying a specified size convolution kernel (filter) on an image. By multiplying that area of the image with the filter, the computer acquires new representative information [15]. websites, and scientific works related to this research.…”

Section: Introductionmentioning

confidence: 99%

Detection of Mask Usage Using Image Processing and Convolutional Neural Network (CNN) Methods

Asri

Sonatha

Rahmayuni

et al. 2022

J. teknol. inf. pendidik.

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Since the first case of COVID-19 was discovered in Indonesia in March 2020, the number of positively contaminated patients is increasing every day. On April 13, 2021, there were 1,571,824 patients who were positively contaminated with COVID-19. Various efforts have been made to suppress the spread of this dangerous virus, one of which is by implementing health protocols that must be obeyed when someone is in a crowd. The recommendation to use masks is a form of health protocol. Often people ignore the importance of using masks in public places, sometimes masks are used not in accordance with justified recommendations. For this reason, research is proposed which will later produce a system that can detect whether someone has used a mask correctly. The system was developed using a deep learning method using a convolutional neural network (CNN). CNN will classify data objects in the form of images / user images and determine whether they are wearing masks correctly or not. The output of the system in the form of audio will be delivered through speakers installed adjacent to the camera which will scan the user's face.

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“…Deep learning is a sub-branch of artificial intelligence and first attracted attention with the ImageNet competition in 2012. Deep learning, which was recognized for its high accuracy in this competition, has been used in many studies such as classification [13,14], detection [15,16], segmentation [17][18][19][20] and prediction [21]. The details of some of the deep learningbased studies used for the classification of scenes in aerial images are given below.…”

Section: Introductionmentioning

confidence: 99%

Classification of Scenes in Aerial Images with Deep Learning Models

İni̇k

2023

Türk Doğa Ve Fen Dergisi

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Automatic classification of aerial images has become one of the topics studied in recent years. Especially for the use of drones in different fields such as agricultural applications, smart city applications, surveillance and security applications, it is necessary to automatically classify the images obtained with the camera during autonomous mission execution. For this purpose, researchers have created new data sets and some computer vision methods have been developed to achieve high accuracy. However, in addition to increasing the accuracy of the developed methods, the computational complexity should also be reduced. Because the methods to be used in devices such as drones where energy consumption is important should have low computational complexity. In this study, firstly, five different state-of-art deep learning models were used to obtain high accuracy values in the classification of aerial images. Among these models, the VGG19 model achieved the highest accuracy with 94.21%. In the second part of the study, the parameters of this model were analyzed and the model was reconstructed. The number of 143.6 million parameters of the VGG19 model was reduced to 34 million. The accuracy of the model obtained by reducing the number of parameters is 93.56% on the same test data. Thus, despite the 66.5% decrease in the parameter ratio, there was only a 0.7% decrease in the accuracy value. When compared to previous studies, the results show improved performance.

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MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models

Cited by 24 publications

References 40 publications

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

Detection of Mask Usage Using Image Processing and Convolutional Neural Network (CNN) Methods

Classification of Scenes in Aerial Images with Deep Learning Models

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