راهکاری مبتنیبر شبکههای عصبی کاملاً کانوولوشنی برای تشخیص همزمان جادهها و ساختمانها در تصاویر هوایی | ||
| نشریه سنجش از دور و GIS ایران | ||
| دوره 13، شماره 2 - شماره پیاپی 50، 1400، صفحه 39-60 اصل مقاله (1.15 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.52547/gisj.13.2.39 | ||
| نویسندگان | ||
| ناصر فرجزاده* 1؛ هیوا ابراهیمزاده2 | ||
| 1دانشیار دانشکدة فنّاوری اطلاعات و مهندسی کامپیوتر، دانشگاه شهید مدنی آذربایجان، تبریز، ایران | ||
| 2دانشجوی کارشناسی ارشد دانشکدة فنّاوری اطلاعات و مهندسی کامپیوتر، دانشگاه شهید مدنی آذربایجان، تبریز، ایران | ||
| چکیده | ||
| توسعة سیستمهای خودکار تشخیص جاده و ساختمان در تصاویر هوایی همواره با چالشهای مهمی مانند متفاوتبودن ظاهر ساختمانها، تغییرات روشنایی، زاویة تصویربرداری و فشرده و چگالبودن جادهها و ساختمانها در نواحی شهری روبهروست. در چند سال اخیر، استفاده از شبکههای عصبی مصنوعی چندلایه (شبکههای عصبی عمیق) مورد توجه بسیاری از پژوهشگران این حوزه (و حوزههای مشابه) قرار گرفته و نتایج خیرهکنندهای با بهکارگیری آنها حاصل شده است. باوجوداین، بهدلیل استفاده از لایههای کاملاً متصل در راهکارهای دادهشده، میانگین مدت زمان پردازش هنوز بسیار زیاد است و مدل ساختهشده نیز بهسرعت دچار پدیدة بیشبرازش میشود. علاوهبراین، در بیشتر روشهای پیشنهادی، برای تفسیر تصاویر هوایی براساس چنین راهکاری از رویکرد تککلاس استفاده شده است. بهعبارتی، تشخیص جادهها و ساختمانها از عوارض طبیعی بهطور همزمان امکانپذیر نیست و لازم است مدلهای جداگانهای برای تشخیص هریک از آنها ایجاد شود. هدف اصلی، در این پژوهش، طراحی معماری جدیدی است که مدل ساختهشده با استفاده از آن بتواند، همزمان، جادهها و ساختمانها را از عوارض طبیعی تشخیص دهد و بهاینترتیب، پیچیدگی عمل طبقهبندی را به حداقل برساند. همچنین، در طراحی معماری پیشنهادی، حذف لایههای کاملاً متصل از معماری چندلایهای مرسوم و در نتیجه، کاهش میانگین مدت زمان پردازش مورد توجه قرار گرفته است. نتایج آزمایشهای انجامگرفته روی بانک تصاویر هوایی ماساچوست نشان میدهد عملکرد معماری پیشنهادی %۳۸ سریعتر از دیگر روشهای مبتنیبر شبکههای عصبی چندلایه بوده است و دقت تشخیص را بهطور میانگین، %۲ افزایش میدهد. | ||
| کلیدواژهها | ||
| یادگیری عمیق؛ شبکههای عصبی مصنوعی؛ شبکههای عصبی کانوولوشنی؛ تصاویر هوایی؛ شناسایی جاده؛ شناسایی ساختمان؛ شناسایی عوارض طبیعی؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| A Fully Convolutional Neural Network-Based Approach for Detecting Simultaneously Roads and Buildings in Aerial Imagery | ||
| نویسندگان [English] | ||
| Nacer Farajzadeh1؛ Hiwa Ebrahimzadeh2 | ||
| 1Associate Prof., Faculty of IT and Computer Engineering Dep., Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| 2M.Sc. Student, Faculty of IT and Computer Engineering Dep., Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| چکیده [English] | ||
| The development of automatic road and building detection systems in aerial imagery are always faced with challenges such as the appearance of buildings, illumination changes, imaging angles, and the density of roads and buildings in urban areas, to name a few. In recent years, employing multi-layered approach in artificial neural networks, known as deep neural networks, has attracted many researchers in this field (and the other fields alike), achieving stunning results. However, the use of fully connected layers in this approach, significantly increases the average processing time and results in an overfitted model. In addition, in most of these methods, a single-class approach has been considered. That is, detecting the roads and the buildings from natural scenes is not possible at the same time, and therefore, it is necessary to build separate binary models for each of them. The main goal of this research is to design a new architecture by which the produced model can be able to simultaneously detect roads and buildings from natural scenes, and thus minimizing the complexity of the classification process. In addition, in the proposed architecture, excluding all fully connected layers from the traditional multi-layered architectures is considered in order to reduce the average processing time. The results of the experiments performed on the Massachusetts dataset, show that the proposed architecture performs 38% faster than the other deep neural network-based methods, and also increases the accuracy by an average of 2%. | ||
| کلیدواژهها [English] | ||
| Deep learning, Artificial neural networks, Convolutional neural networks, Aerial imagery, Road detection, Building detection, Natural scene detection, Artificial intelligence | ||
| مراجع | ||
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