تشخیص نوع خودرو به‌منظور کنترل هوشمند عبور و مرور خودروها توسط یادگیری عمیق | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| دوره 2، شماره 2، اسفند 1403، صفحه 111-124 اصل مقاله (1.33 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2025.22898.1112 | ||
| نویسندگان | ||
| مرتضی احساندوست1؛ عاطفه سلیمی شهرکی* 2؛ محمد روح ا... یزدانی3؛ محمد لعلی دستجردی1 | ||
| 1دانشجوی کارشناسی ارشد رشته مهندسی برق، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، اصفهان، ایران | ||
| 2استادیار رشته مهندسی برق، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، اصفهان، ایران | ||
| 3دانشیار رشته مهندسی برق، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، اصفهان، ایران | ||
| چکیده | ||
| با افزایش خودروها در سراسر جهان و توسعه فناوری، سیستم‌های حمل‌ونقل هوشمند به‌عنوان یکی از راه‌حل‌های کلیدی برای کنترل ترافیک و افزایش ایمنی راه‌ها مطرح‌شده‌اند. یکی از بخش‌های مهم این سیستم‌ها، تشخیص نوع خودرو است. در این مقاله از ترکیب مدل‌های EfficientNet-B0 و YOLO-V11 جهت تشخیص نوع خودرو استفاده‌شده است. در این مدل، EfficientNet-B0 به‌عنوان ستون فقرات مدل YOLO-V11 به‌کاررفته است. این مدل، ویژگی‌های تصاویر را استخراج کرده و آن‌ها را به مدل YOLO اعمال می‌کند تا مکان و نوع خودرو را به‌طور دقیق تشخیص دهد. برای ارزیابی عملکرد مدل پیشنهادی از مجموعه داده تصویری BVMMR که شامل بیش از 5000 تصویر از انواع خودروهای ایرانی است، استفاده‌شده است. مدل مورداستفاده با زبان برنامه‌نویسی پایتون نوشته‌شده و در محیط کولی (Colab) اجراشده است. نتایج اجرای کدها نشان می‌دهد که مدل پیشنهادی دارای مقدار میانگین دقت در هم‌پوشانی پنجاه‌درصد (mAP50) برابر با 3/99% و میانگین دقت در هم‌پوشانی بیشتر از پنجاه‌درصد (mAP50-95) برابر با 3/98% است. این مدل، ازنظر سرعت پردازش نیز توانسته است با میانگین زمان 2/0 میلی‌ثانیه برای پیش‌پردازش، 8/2 میلی‌ثانیه برای استنتاج و 5/2 میلی‌ثانیه برای پس‌پردازش در هر تصویر، عملکرد مطلوبی را ارائه دهد. | ||
| کلیدواژه‌ها | ||
| بینایی رایانه؛ سیستم حمل‌ونقل هوشمند؛ یادگیری عمیق؛ YOLO؛ Efficient Net | ||
| عنوان مقاله [English] | ||
| Vehicle Models Recognition for Intelligent Control of Vehicle Traffic by Deep Learning | ||
| نویسندگان [English] | ||
| Morteza Ehsandoust1؛ Atefeh Salimi Shahraki2؛ Mohammad Rohollah Yazdani3؛ Mohammad Lali Dastjerdi1 | ||
| 1MSc. student Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran | ||
| 2Assistant Professor Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran | ||
| 3Associate Professor Department of Electrical Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran | ||
| چکیده [English] | ||
| With the increasing number of vehicles worldwide and the advancement of technology, intelligent transportation systems have emerged as a key solution for traffic control and road safety enhancement. One of the crucial components of these systems is vehicle type recognition. In this paper, a combination of EfficientNet-B0 and YOLO-V11 models is used for vehicle type classification. In this model, EfficientNet-B0 serves as the backbone of YOLO-V11, extracting image features and feeding them into the YOLO model to accurately determine the location and type of vehicles. To evaluate the performance of the proposed model, the BVMMR image dataset—containing over 5,000 of various Iranian vehicles—was utilized. The model was implemented using Python and executed in the Colab online environment. The results demonstrate that the proposed model achieves a mean Average Precision at 50% IoU (mAP50) of 99.3% and a mean Average Precision across IoU thresholds from 50% to 95% (mAP50-95) of 98.3%. Additionally, in terms of processing speed, the model delivers an efficient performance with an images average preprocessing time of 0.2 milliseconds, inference time of 2.8 milliseconds, and post-processing time of 2.5 milliseconds per image. | ||
| کلیدواژه‌ها [English] | ||
| Computer Vision, Intelligent Transportation System, Deep Learning, YOLO, Efficient Net | ||
| مراجع | ||
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