کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4947860 1439592 2017 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Plant identification using deep neural networks via optimization of transfer learning parameters
ترجمه فارسی عنوان
شناسایی گیاه با استفاده از شبکه های عصبی عمیق از طریق بهینه سازی پارامترهای یادگیری انتقال
کلمات کلیدی
شبکه های عصبی انعقادی، یادگیری عمیق، شناسایی کارخانه، انتقال یادگیری، نمره رتبه معکوس،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
We use deep convolutional neural networks to identify the plant species captured in a photograph and evaluate different factors affecting the performance of these networks. Three powerful and popular deep learning architectures, namely GoogLeNet, AlexNet, and VGGNet, are used for this purpose. Transfer learning is used to fine-tune the pre-trained models, using the plant task datasets of LifeCLEF 2015. To decrease the chance of overfitting, data augmentation techniques are applied based on image transforms such as rotation, translation, reflection, and scaling. Furthermore, the networks' parameters are adjusted and different classifiers are fused to improve overall performance. Our best combined system has achieved an overall accuracy of 80% on the validation set and an overall inverse rank score of 0.752 on the official test set. A comparison of our results against the results of the LifeCLEF 2015 plant identification campaign shows that we have improved the overall validation accuracy of the top system by 15% points and its overall inverse rank score on the test set by 0.1 while outperforming the top three competition participants in all categories. The system recently obtained a very close second place in the PlantCLEF 2016.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 235, 26 April 2017, Pages 228-235
نویسندگان
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