Comparison of deep learning models in terms of multiple object detection on satellite images

Doğan, Ferdi; Türkoğlu, İ̇brahim

doi:10.36909/jer.12843

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…While the low resolution causes the features to become incomprehensible and indistinguishable, the situation where the resolution is above the required increases the processing load for the analyses to be made. Thanks to the increasing resolution with the developing imaging technology, the objects in the images obtained for remote sensing have become much more distinguishable and much more useful for classification [10,22]. These conveniences in image access in remote sensing technology attract the attention of experts working on classification studies.…”

Section: Introductionmentioning

confidence: 99%

Parallel Convolutional Neural Networks and Transfer Learning for Classifying Landforms in Satellite Images

Atik¹

2023

ITC

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The use of remote sensing has great potential for detecting many natural differences, such as disasters, climate changes, and urban changes. Due to technological advances in imaging, remote sensing has become an increasingly popular topic. One of the significant benefits of technological advancement has been the ease with which remote sensing data is now accessible. Physical and spatial information is detected by remote sensing, which can be described as the process of identifying distinctive characteristics of an environment. Resolution is one of the most important factors influencing the success of the detection processes. As a result of the resolution being below the necessary level, features of the objects to be differentiated become incomprehensible and therefore constitute a significant barrier to differentiation. The use of deep learning methods for classifying remote sensing data has become prevalent and successful in recent years. This study classified Satellite images using deep learning and machine learning methods. Based on the transfer learning strategy, a parallel convolutional neural network (CNN) was designed in the study. To improve the feature mapping of an image, convolutional branches use pre-trained knowledge of the transmitted network. Using the offline augmentation method, the raw data set was balanced to overcome its unbalanced class distribution and increased network performance. A total of 35 classes of landforms have been studied in the experiments. The accuracy value of the developed model in the classification study of landforms was 97.84%. According to experimental results, the proposed method provides high classification accuracy in detecting landforms and outperforms existing studies.

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Section: Introductionmentioning

confidence: 99%

Parallel Convolutional Neural Networks and Transfer Learning for Classifying Landforms in Satellite Images

Atik¹

2023

ITC

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Light-Weight Deep Learning Framework for Automated Remote Sensing Images Classification

Adegun

Viriri

Tapamo

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models

Aktaş,

Doğan,

Türkoğlu

2024

Sakarya University Journal of Computer and Information Sciences

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Electricity generation from renewable energy sources such as solar energy has come to the forefront in the last decade. The solar energy cell is an indispensable part of the solar energy ecosystem of solar panels, and defective cells cause financial losses in energy production. Experienced experts are needed to detect defects on solar cells. Autonomous systems are important to accelerate the process. Classical image processing techniques are used to manually detect defects on cells. To use these techniques, many parameters are need to be entered into EL imaging software. However, in this study, these processes were carried out automatically without the need for external intervention. False detection/classification may occur during the processes performed by EL imaging devices due to weakness of the operator experience or EL imaging software. It is aimed to use automatic image processing and then deep learning techniques to achieve faster and higher performance than the results obtained from EL imaging devices using classic image processing techniques. AI algorithm and deep learning models can be an important solution. In this study, two AI algorithm and 10 different deep learning models were used to classify solar cells. EL images of defective and normal solar cells with 4 and 5 busbars were used in the study. The dataset, includes 9360 images of solar cells, 4680 of which are defective and 4680 are normal. Performance evaluation of the models made according to the confusion matrix. According to the results, Mobilenet-v2 and VGG-19 achieved the highest validation accuracy rate of 99.68%. According to F1-score, Mobilenetv2 achieved the highest performance of 99.73%. It has been shown that the Mobilenet-v2 is slightly more successful than other models in terms of validation and F1-score. The results show that trained DL models can be used as an inspection method in the production line of solar panels and cells.

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Comparison of deep learning models in terms of multiple object detection on satellite images

Cited by 3 publications

References 41 publications

Parallel Convolutional Neural Networks and Transfer Learning for Classifying Landforms in Satellite Images

Parallel Convolutional Neural Networks and Transfer Learning for Classifying Landforms in Satellite Images

Light-Weight Deep Learning Framework for Automated Remote Sensing Images Classification

Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models

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