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شناسایی مواد غذایی ایرانی در تصاویر با استفاده از یادگیری عمیق | ||
مهندسی بیوسیستم ایران | ||
دوره 54، شماره 3، مهر 1402، صفحه 19-41 اصل مقاله (2.28 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijbse.2023.366560.665526 | ||
نویسندگان | ||
زهرا حاج علی اوغلی؛ سلیمان حسین پور* ؛ سید سعید محتسبی | ||
گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
چکیده | ||
سبک زندگی سالم و رژیم غذایی متعادل نقش حیاتی در حفظ سلامت انسانها ایفا میکند. در این دوره از تغییر سریع سبک زندگی و فناوری، یک سیستم تشخیص و بخشبندی مواد غذایی مبتنی بر موبایل که مواد غذایی را شناسایی کند، میتواند بسیار مفید باشد و عادات غذایی را بهبود بخشد. در این مقاله یک سیستم جدید ارائه شده است که با دریافت تصویر ورودی، مواد غذایی داخل تصویر را تشخیص و بخشبندی میکند. این سیستم از تکنیکها و مدلهای یادگیری عمیق استفاده میکند. الگوریتم مورد استفاده YOLO است که با بهرهمندی از روشهای ساده مبتنی بر رگرسیون، توانایی تشخیص و بخشبندی مواد غذایی را با یک گذر از شبکه فراهم میآورد که با هدف بهبود دقت و سرعت در تشخیص ارائه شده است. این روشها شامل استفاده از YOLOv7 برای تشخیص مواد غذایی و استفاده از بخشبندی نمونهای YOLOv5، YOLOv7 و YOLOv8 برای بخشبندی تصاویر است. علاوه بر این، مجموعهدادهای از غذاهای ایرانی حاوی مواد غذایی مختلف تهیه و مورد استفاده قرار گرفت. بر اساس نتایج، مقادیر دقت، یادآوری و دقت متوسط میانگین YOLOv7 به ترتیب 844/0، 924/0 و 932/0 به دست آمد. همچنین، عملکرد بخشبندی نمونهای YOLOv7 نسبت به YOLOv5 و YOLOv8 بهتر بود که مقادیر دقت بخشبندی، یادآوری و دقت متوسط میانگین 5/0 برای YOLOv7 به ترتیب 959/0، 943/0 و 906/0 است. نتایج حاکی از آن هستند که روش پیشنهاد شده در این مقاله دقت بالا در تشخیص مواد غذایی ایرانی و همچنین سرعت و دقت بالا در بخشبندی نمونهای را فراهم میکند. بنابراین با استفاده از الگوریتم YOLO، میتوان غذاهای ایرانی را با دقت بالا تشخیص داد و تصاویر آنها را تقسیم بندی کرد. این پژوهش از طریق تکنولوژی هوشمند و الگوریتمهای جدید یادگیری عمیق به ترویج سبک زندگی سالم از طریق تکنولوژی هوشمند در ایران میپردازد. | ||
کلیدواژهها | ||
تشخیص مواد غذایی؛ تقسیمبندی نمونهای؛ یادگیری عمیق؛ YOLOv7 | ||
عنوان مقاله [English] | ||
Detection of Iranian foods in images using deep learning | ||
نویسندگان [English] | ||
zahra hajalioghli؛ Soleiman Hosseinpour؛ Seyed Saeid Mohtasebi | ||
Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran | ||
چکیده [English] | ||
Maintaining the well-being of individuals is greatly influenced by a healthy lifestyle and balanced diet. The identification and segmentation of food items can be improved by utilizing a mobile-based system in this era of rapid lifestyle changes and technology. This article introduces a novel system that, upon receiving input images, detects and segmentation the food items within the images. The system utilizes deep learning techniques and models, employing the YOLO algorithm. By incorporating regression-based simple methods, the system achieves the capability to detect and categorize food items in a single pass through the network, aiming to enhance accuracy and speed in the detection process. YOLOv7 was employed for food detection and YOLOv5, YOLOv7, and YOLOv8 was utilized for image segmentation. Based on the results, the accuracy, recall, and average precision values for YOLOv7 were 0.844, 0.924, and 0.932, respectively. Furthermore, the instance segmentation performance of YOLOv7 outperformed YOLOv5 and YOLOv8, with precision, recall, and mean average precision values of 0.959, 0.943, and 0.906, respectively. These findings underscore the high accuracy in detecting Iranian foods and the remarkable speed and precision in food image segmentation attainable through advanced deep-learning algorithms. Consequently, this study establishes that accurate detection of Iranian foods can be accomplished through the utilization of sophisticated deep-learning techniques. This research focuses on promoting a healthy lifestyle through intelligent technology and novel deep learning algorithms in Iran. | ||
کلیدواژهها [English] | ||
Deep learning, Food detection, Instance segmentation, YOLOv7 | ||
مراجع | ||
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