تشخیص خطوط جاده با استفاده از معماری سبک‌وزن مبتنی بر ماژول توجه CBAM

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی ارشد گروه مهندسی کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران

2 استادیار گروه مهندسی کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران

3 استادیار گروه مهندسی فناوری اطلاعات، دانشگاه سیستان و بلوچستان، زاهدان، ایران

10.22034/abmir.2026.24147.1209

چکیده

تشخیص خطوط جاده به‌عنوان عناصر حیاتی در سیستم‌های ادراک بصری خودروهای هوشمند، نقشی کلیدی در تضمین ناوبری ایمن ایفا می‌کنند و برای سیستم‌های رانندگی خودران و فناوری‌های پیشرفته کمک‌راننده ضروری هستند. بااین‌حال، دستیابی به‌دقت بالا در تشخیص خطوط جاده در شرایط چالش‌برانگیز مانند سایه، ازدحام ترافیکی و محوشدگی خطوط جاده، در عین حفظ کارایی بلادرنگ، همچنان یک چالش اساسی به شمار می‌رود. در این پژوهش، یک معماری سبک‌وزن به‌منظور ارتقای عملکرد تشخیص خطوط جاده در محیط‌های پیچیده ارائه شده است که با تلفیق راهبردهای نوآورانه طراحی گردیده است. هسته اصلی این چارچوب، ادغام هدفمند ماژول توجه کانالی–فضایی در لایه‌های میانی شبکه ResNet است که امکان تقویت و پالایش ویژگی‌های متمایزکننده را فراهم می‌سازد. افزون بر این، با بهره‌گیری از راهبرد پیشرفته تقطیر دانش، مدل پیشنهادی به دقت تشخیص بالا همراه با کارایی محاسباتی بلادرنگ دست‌یافته است. ارزیابی روی مجموعه‌داده CULane نشان داد که روش پیشنهادی با امتیاز F1 معادل %‌۲۰/۸۰  و سرعت ۴۰۷ فریم بر ثانیه، به ترتیب بهبود %۵۴/۰ و افزایش سرعت %۷۹/۱ نسبت به مدل CLRKDNet و روش ECBAM_ASPP داشته است. همچنین در مجموعه‌داده TuSimple، مدل پیشنهادی بادقت%۹۶/۹۶ کمترین خطای منفی کاذب %۵۷/۱ در میان روش‌های مقایسه شده، عملکرد برتری از خود نشان داده است. در مقایسه با روش‌های مبتنی بر توجه مانند ECBAM_ASPP و معماری‌های پرسرعتی همچون UFLD، روش پیشنهادی تعادل مطلوبی میان دقت و سرعت برقرار کرده و برای کاربردهای بلادرنگ در خودروهای خودران مناسب‌تر است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Road Lane Detection Using a Lightweight Architecture Based on the CBAM Attention Module

نویسندگان [English]

  • Rafat Moaiery Far 1
  • Masoumeh Rezaei 2
  • Nik-Mohammad Balouchzahi 3
1 Master's student, Department of Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 Assistant Professor, Department of Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran
3 Assistant Professor, Department of Information Technology, University of Sistan and Baluchestan, Zahedan, Iran
چکیده [English]

Lane detection is a critical perception task in intelligent vehicle systems, playing a fundamental role in ensuring safe navigation for autonomous driving and advanced driver-assistance systems (ADAS). However, achieving high detection accuracy under challenging conditions—such as shadows, traffic congestion, and faded lane markings—while maintaining real-time performance remains a significant challenge. In this study, a lightweight architecture is proposed to enhance lane detection performance in complex environments through the integration of innovative strategies. The core of the proposed framework lies in the targeted incorporation of the Convolutional Block Attention Module (CBAM) into the intermediate layers of ResNet, enabling effective refinement of discriminative feature representations. Furthermore, by leveraging an advanced knowledge distillation strategy, the model achieves both high detection accuracy and real-time computational efficiency. Experimental evaluation on the CULane dataset demonstrates that the proposed method achieves an F1-score of 80.20% with a processing speed of 407 frames per second, representing improvements of 0.54% in accuracy and 1.79% in speed compared to CLRKDNet and ECBAM_ASPP, respectively. Furthermore, on the TuSimple dataset, the proposed model attains an accuracy of 96.96% while achieving the lowest false-negative rate of 1.57% among the compared methods. Compared with attention-based approaches such as ECBAM_ASPP and high-speed architectures such as UFLD, the proposed method achieves a superior balance between detection accuracy and computational efficiency, making it more suitable for real-time deployment in autonomous vehicles.

کلیدواژه‌ها [English]

  • Road Lane Detection
  • Advanced Driver-Assistance Systems
  • Autonomous Driving
  • Convolutional Block Attention Module
  • Knowledge Distillation
  • Transportation Safety

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پژوهش های نظری و کاربردی هوش ماشینی
دوره 4، شماره 1
بهار و تابستان 1405
اردیبهشت 1405
صفحه 147-165

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