شبکه سریع و کم‌وزن برای شناسایی خطوط جاده با استفاده از معماری موبایل‌نت و توابع هزینه متفاوت
محورهای موضوعی : مهندسی برق و کامپیوتر
پژمان گودرزی
1
,
میلاد حیدری
2
,
مهدی حسین پور
3
1 - پژوهشکده فناوری اطلاعات، پژوهشگاه ارتباطات و فناوری اطلاعات
2 - دانشکده فناوری اطلاعات و مهندسی کامپیوتر، دانشگاه شهید مدنی آذربایجان
3 - پژوهشکده فناوری اطلاعات، پژوهشگاه ارتباطات و فناوری اطلاعات
کلید واژه: تشخیص خط, خودروی خودران, موبایل‌نت, یادگیری عمیق,
چکیده مقاله :
با استفاده از سیستم تشخیص خط در خودروهای خودران می‌توان موقعیت نسبی خودرو را نسبت به دیگر خودروها و همچنین احتمال خروج از خط و حتی امکان تصادف را بررسی کرد. در این مقاله، یک رویکرد تشخیص خط کم‌وزن و سریع برای تصاویر برگرفته‌شده از دوربین تعبیه‌گردیده در شیشه جلویی خودروها ارائه شده است. بیشتر روش‌های موجود، مسئله تشخیص خط را به صورت کلاس‌بندی در سطح پیکسل در نظر می‌گیرند. این روش‌ها با وجود داشتن قدرت تشخیص بالا، از دو ضعف داشتن پیچیدگی محاسباتی بالا و عدم توجه به اطلاعات محتوایی کلی تصویر منحصربه‌فرد خطوط (در نتیجه در صورت وجود مانع، امکان تشخیص ندارند) رنج می‌برند. روش پیشنهادی پیش رو با بهره‌گیری از روش انتخاب بر اساس ردیف، وجود خطوط در هر ردیف را بررسی می‌کند. همچنین استفاده از معماری موبایل‌نت باعث به‌دست‌آمدن نتایج خوب با تعداد پارامترهای یادگیری کمتر شده است. استفاده از سه تابع مختلف به عنوان توابع هزینه با اهداف متفاوت، باعث به‌دست‌آمدن نتایج عالی و درنظرگرفتن اطلاعات محتوایی کلی منحصربه‌فرد خطوط در کنار اطلاعات محلی شده است. آزمایش‌های انجام‌گرفته بر روی مجموعه تصاویر ویدئویی TuSimple نشان از عملکرد مناسب رویکرد پیشنهادی از لحاظ کارایی و مخصوصاً از لحاظ سرعت دارد.
By using the line detection system, the relative position of the self-driving cars compared to other cars, the possibility of leaving the lane or an accident can be checked. In this paper, a fast and lightweight line detection approach for images taken from a camera installed in the windshield of cars is presented. Most of the existing methods consider the problem of line detection in the form of classification at the pixel level. These methods despite having high accuracy, suffer from two weaknesses of having the high computational cost and not paying attention to the general lines content information of the image (as a result, they cannot detect if there is an obstacle). The proposed method checks the presence of lines in each row by using the row-based selection method. Also, the use of Mobile-net architecture has led to good results with fewer learning parameters. The use of three different functions as cost functions, with different objectives, has resulted in obtaining excellent results and considering general content information along with local information. Experiments conducted on the TuSimple video image collection show the suitable performance of the proposed approach both in terms of efficiency and especially in terms of speed.
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