توسعه الگوریتم یادگیری عمیق به منظور تشخیص و طبقه‌بندی هوشمند گونه‌های ماهی کپور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک بیوسیستم دانشگاه لرستان

2 فارغ التحصیل مقطع دکتری، گروه ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران

3 استادیار، گروه علوم دامی، دانشگاه لرستان

چکیده

چکیده: شناسایی گونه­های ماهیان برای صنایع آبزی پروری و صید، مدیریت ذخایر پهنه های آبی و نظارت زیست محیطی آبزیان حیاتی می باشد. در این مطالعه، شبکه عصبی یادگیری عمیق به عنوان روشی غیرمخرب و برخط جهت تشخیص چهار گونه مهم و اقتصادی خانواده کپورماهیان شامل کپور معمولی، کپور علفخوار، کپور سرگنده و کپور نقره­ای ایجاد و مورد استفاده قرار گرفت. به این منظور، ساختار شبکه پیش آموزش دیده VGG-19 (Visual Geometry Group-19) توسط لایه­های پولینگ، تماما متصل، نرمال­سازی و رهاسازی بروزرسانی گردید. از 409 تصویر برای آموزش و ارزیابی مدل توسعه داده شده استفاده گردید. مقادیر میانگین دقت، صحت، حساسیت، اختصاصی بودن و سطح زیر منحنی به ازای هر کلاس به ترتیب برابر با 39/98، 87/96، 87/96، 96/98 و 92/97 درصد حاصل شد. سطح بالای دقت بدست آمده بدلیل توانایی مدل عمیق پیشنهادی در ساخت ویژگی های خودآموز سلسله مراتبی است که در تطابق با ویژگی­های مورد استفاده در شناسایی ماهیان بود.

کلیدواژه‌ها


عنوان مقاله [English]

Deep Learning Algorithm Development for Intelligent Detection and Classification of Carp Species

نویسندگان [English]

  • Amin Taheri-Garavand 1
  • amin nasiri 2
  • َAshkan Banan 3
1 Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran.
2 Ph.D Graduated, Mechanics of Agricultural Machinery Dept., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
3 Assistant Professor, Department of Animal Scinence, Lorestan University, Khorramabad, Iran.
چکیده [English]

ABSTRACT: Identifying fish species is critical for aquaculture and fishery industries, managing aquatic stocks and environmental monitoring of aquatics. In this study, deep learning neural network as a non-destructive and real-time approach was developed and used to identify four economically important species of carp family including common carp, grass carp, bighead carp and silver carp. For this purpose, the architecture of pre-trained VGG19 (Visual Geometry Group-19) was updated by pooling, fully-connected, normalization and dropout layers. 409 images were used for training and evaluating the developed model. The mean value of accuracy, precision, sensitivity, specificity and AUC parameters was calculated as 98.39, 96.87, 96.87, 98.96, and 97.92%, respectively. The obtained high level of accuracy is due to the ability of the proposed deep model in constructing a hierarchy of self-learned features which was consistent with the hierarchy of fish identification keys.

کلیدواژه‌ها [English]

  • : Deep learning
  • classification
  • Cyprinidae
  • Feature visualization
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