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Register a new userالتصنيف التلقائي للهياكل الجيولوجية باستخدام الرؤية الحاسوبية ونموذج التعلم العميق
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دراسة الهياكل الجيولوجية المكشوفة على سطح الأرض ذات أهمية كبيرة بشكل عام وخصوصا في التصميم الهندسي والبناء. في هذا البحث ، استخدمنا 2206 صورة مع 12 ملصق للتعرف على الهياكل الجيولوجية بناءً على نموذج Inception-v3. تم اعتماد الصور ذات التدرج الرمادي واللون في النموذج. كما تم بناء نموذج الشبكة العصبية التلافيفية (CNN) وتم تطبيق خوارزمية أقرب جار (KNN) والشبكة العصبية الاصطناعية (ANN) وتعزيز التدرج الشديد (XGBoost) في تصنيف الهياكل الجيولوجية بناءً على الميزات المستخرجة من مكتبة رؤية الكمبيوتر مفتوحة المصدر (OpenCV). أخيرًا ، تمت مقارنة أداء الطرق الخمس وأظهرت النتائج أن أداء KNN و ANN و XGBoost كان ضعيفًا وبدقة أقل من 40.0٪. أما CNN فعد عانت من فرط التدريب Overfitting. كان للنموذج الذي تم تدريبه باستخدام التعلم بالنقل تأثير كبير على مجموعة بيانات صغيرة من صور التركيب الجيولوجي. وأفضل نموذجين وصلوا إلى دقة 83.3٪ و 90.0٪ على التوالي. هذا يدل على أن النسيج هو السمة الرئيسية في هذا البحث. يمكن أن يستخرج التعلم القائم على نموذج التعلم العميق ميزات بيانات البنية الجيولوجية الصغيرة بشكل فعال ، وهو قوي في تصنيف صور الهيكل الجيولوجي.
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Fisher, M.A.; Normark, W.R.; Greene, H.G.; Lee, H.J.; Sliter, R.W. Geology and tsunamigenic potential of submarine landslides in Santa Barbara Channel, Southern California. Mar. Geol. 2005, 224, 1–22.
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