İnsansız Hava Araçları ve Uydu Görüntülerinden Elde Edilen Veri Seti ile Havaalanlarının Tespitinin Yapılmasında SSD ve Faster R-CNN Algoritmalarının Karşılaştırılması

Zeren, Muhammed Taha; Aytulun, Sabahattin Kerem; Kırelli, Yasin

doi:10.31590/ejosat.742789

Cited by 7 publications

(1 citation statement)

References 7 publications

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“…With this, it can be said that the SSD MobileNet model performed well compared to the Faster R-CNN model in classifying the seeds. However, the models' performance obtained in this study was different from the performance obtained in the study by Zeren et al [25]. In their study, the Faster R-CNN model performed better than the SSD MobileNet model in detecting an airport.…”

Section: Performance Evaluation and Model Comparison In Seed Germinat...contrasting

confidence: 99%

Enhancing Seed Germination Test Classification for Pole Sitao (Vigna unguiculata (L.) Walp.) Using SSD MobileNet and Faster R-CNN Models

Brutas,

Fajardo,

Quilloy

et al. 2024

Applied Sciences

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The classification of germinated pole sitao (Vigna unguiculata (L.) Walp.) seeds is important in seed germination tests. The automation of this process has been explored for different grain and legume seeds but is only limited to binary classification. This study aimed to develop a classifier system that can recognize three classes: normal, abnormal, and ungerminated. SSD MobileNet and Faster R-CNN models were trained to perform the classification. Both were trained using 1500 images of germinated seeds at fifth- and eighth-day observations. Each class had 500 images. The trained models were evaluated using 150 images per class. The SSD MobileNet model had an accuracy of 0.79 while the Faster R-CNN model had an accuracy of 0.75. The results showed that the average accuracies for the classes were significantly different from one another based on one-way ANOVA at a 95% confidence level with an F-critical value of 3.0159. The SSD MobileNet model outperformed the Faster R-CNN model in classifying pole sitao seeds, with improved precision in identifying abnormal and ungerminated seeds on the fifth day and normal and ungerminated seeds on the eighth day. The results confirm the potential of the SSD MobileNet model as a more reliable classifier in germination tests.

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Section: Performance Evaluation and Model Comparison In Seed Germinat...contrasting

confidence: 99%

Enhancing Seed Germination Test Classification for Pole Sitao (Vigna unguiculata (L.) Walp.) Using SSD MobileNet and Faster R-CNN Models

Brutas,

Fajardo,

Quilloy

et al. 2024

Applied Sciences

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Doctors Versus YOLO: Comparison Between YOLO Algorithm, Orthopedic and Traumatology Resident Doctors and General Practitioners on Detection of Proximal Femoral Fractures on X-ray Images with Multi Methods

Zeren¹,

Arslankaya²,

Cam³

et al. 2024

Int. J. Artif. Intell. Tools

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In the 1950s, the concept of artificial intelligence emerged, suggesting that machines could possess the ability to think and learn. In the 21st century, with advancements in GPUs and CPUs, deep learning has become an integral part of human life. Proximal femoral fractures are known to be one of the leading causes of mortality and injuries among the elderly population. This study aims to detect proximal femoral fractures in X-ray images and compare the success of using the YOLOv4 algorithm and provide decision support system within the diagnosis. To retrain the algorithm, more than 500 patients’ X-ray images were examined. Through data augmentation techniques, the initial set of 410 patients’ femur proximal fracture X-ray images was expanded to 820 images. After retraining the YOLO algorithm, two different groups were included for comparing the algorithm’s performance: orthopedic specialists and general practitioners. The results from these three groups were evaluated using specific criteria. The YOLOv4 model demonstrated an accuracy of 90.33%. In comparison, orthopedic and traumatology resident doctors achieved an accuracy of 91.42%, while the general practitioner group achieved an accuracy of 81.30%.

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Pipeline Multitype Artifact Recognition Method Based on Inception_Resnet _V2 Structure Improving SSD Network

Zheng

2022

Advances in Multimedia

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A fast recognition method for assembly line workpieces based on an improved SSD model is proposed to address the problems of low detection accuracy and lack of real-time performance when existing target detection models face small-scale targets and stacked targets. Based on the SSD network, the optimized Inception_Resnet _V2 structure is used to improve its feature extraction layer and enhance the extraction capability of the network for small-scale targets. The repulsion loss (Reploss) is used to optimize the loss function of the SSD network to solve the problem of stacked workpieces. The issue of difficult detection is improved. The robustness of the algorithm is enhanced. The experimental results show that the improved SSD target detection method improves the detection accuracy by 9.69% over the traditional SSD map. The detection speed meets the real-time requirements, which is a better balance of detection real time and accuracy requirements. The algorithm can recognize small-scale and stacked targets with higher category confidence, better algorithm robustness, and better recognition performance compared to the same type of target detection algorithms.

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İnsansız Hava Araçları ve Uydu Görüntülerinden Elde Edilen Veri Seti ile Havaalanlarının Tespitinin Yapılmasında SSD ve Faster R-CNN Algoritmalarının Karşılaştırılması

Cited by 7 publications

References 7 publications

Enhancing Seed Germination Test Classification for Pole Sitao (Vigna unguiculata (L.) Walp.) Using SSD MobileNet and Faster R-CNN Models

Enhancing Seed Germination Test Classification for Pole Sitao (Vigna unguiculata (L.) Walp.) Using SSD MobileNet and Faster R-CNN Models

Doctors Versus YOLO: Comparison Between YOLO Algorithm, Orthopedic and Traumatology Resident Doctors and General Practitioners on Detection of Proximal Femoral Fractures on X-ray Images with Multi Methods

Pipeline Multitype Artifact Recognition Method Based on Inception_Resnet _V2 Structure Improving SSD Network

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