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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2024 / Cilt: 39 Sayı: 4

mobilenet based traffic sign detection system for mobile mapping: crowdsourced geographical data collection system

Mobil haritalama amaçlı Mobilenet tabanlı trafik işaretleri tespit sistemi: kitlesel coğrafi bilgi toplama sistemi

Ceren Özcan Tatar(ESKİŞEHİR TEKNİK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ, UZAKTAN ALGILAMA VE COĞRAFİ BİLGİ SİSTEMLERİ ANABİLİM DALI (DİSİPLİNLERARASI))0000-0003-4075-8329
Emrah Yılmaz(Başarsoft Bilgi Teknolojileri A.Ş.)0000-0001-8850-8199
Abdullah Efe(Başarsoft Bilgi Teknolojileri A.Ş.)0000-0002-7362-280X
Berk Sönmez(Başarsoft Bilgi Teknolojileri A.Ş.)0000-0002-5677-0448
Yalçın Özdemir(Başarsoft Bilgi Teknolojileri A.Ş.)0000-0003-4348-3180
Burak Danışan(Başarsoft Bilgi Teknolojileri A.Ş.)0009-0007-5377-9600
Hale İrem Beyaz(Başarsoft Bilgi Teknolojileri A.Ş.)0009-0009-6406-0292
Engin Yegnidemir(GEBZE TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR MÜHENDİSLİĞİ ANABİLİM DALI)0000-0002-6544-9496
Pages
2305–2315
Publication Date
2024-05-20
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study demonstrates that CGDCS is more practical and efficient than traditional methods involving manual processing, semi-traditional methods based on the extraction of shape and color features of traffic signs, and AI-based methods that process field data in high-performance computers using high computer vision and machine learning techniques.

📌 3 Key Takeaways

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Objective & Hypothesis:Mobile mapping systems (MMS) have gained increasing interest as a cost-effective means of collecting geospatial data, catering to the digital mapping needs of various domains such as advanced driver assistance systems (ADAS) and intelligent transportation systems (ITS).
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Key Findings & Data:In the generated maps, the location and class information of traffic signs are particularly crucial for the aforementioned applications.
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Conclusion & Impact:The study demonstrates that CGDCS is more practical and efficient than traditional methods involving manual processing, semi-traditional methods based on the extraction of shape and color features of traffic signs, and AI-based methods that process field data in high-performance computers using high computer vision and machine learning techniques.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:kitlesel coğrafi veri toplama sistemi (KCVTS) · makine öğrenmesi · trafik işaretleri

Abstract

Mobile mapping systems (MMS) have gained increasing interest as a cost-effective means of collecting geospatial data, catering to the digital mapping needs of various domains such as advanced driver assistance systems (ADAS) and intelligent transportation systems (ITS). In the generated maps, the location and class information of traffic signs are particularly crucial for the aforementioned applications. However, the extensive and complex nature of data collected by MMS makes it challenging to infer the location and class of traffic signs. Consequently, researchers have developed artificial intelligence-based methods for processing traffic sign data. In this study, a Crowdsourced Geographical Data Collection System (CGDCS) which is designed for the inference of traffic sign location and class information using artificial intelligence is introduced. CGDCS is a lightweight system that operates on mobile devices, leveraging the MobileNet architecture to detect and classify traffic signs present in real-time camera images, thereby transferring the location and class information of the signs to a database. The study demonstrates that CGDCS is more practical and efficient than traditional methods involving manual processing, semi-traditional methods based on the extraction of shape and color features of traffic signs, and AI-based methods that process field data in high-performance computers using high computer vision and machine learning techniques.

Özet

Mobil haritalama sistemleri (Mobile Mapping Systems- MMS) coğrafi veri toplama yetenekleri ile birlikte, gelişmiş sürücü destek sistemleri (Advanced Driver Assistance Systems- ADAS) ve akıllı ulaşım sistemleri (Intelligent Transportation Systems - ITS) gibi birçok uygulama alanın sayısal harita ihtiyacını karşılayabilmektedir. Üretilen haritalarda özellikle trafik işaretlerinin konum ve sınıf bilgilerinin bulunması, bahsi geçen uygulama alanları için önem arz etmektedir. Ancak, MMS tarafından toplanan verilerin geniş ölçekli ve karmaşık olması, trafik işaretlerinin konum-sınıf çıkarımlarını zorlaştırmaktadır. Bu nedenle araştırmacılar, trafik işareti verilerinin işlenmesi için yapay zekâ tabanlı yöntemler geliştirmiştir. Bu çalışmada, trafik işaretlerinin konum ve sınıf bilgilerinin yapay zekâ ile çıkarımına yönelik tasarlanan Kitlesel Coğrafi Bilgi Toplama Sistemi (KCVTS) açıklanmıştır. KCVTS; MobileNet tabanıyla mobil cihazlarda etkinlik gösteren, cihazın gerçek-zamanlı kamera görüntülerinde bulunan trafik işaretlerini tespit eden ve sınıflandıran ve böylece, işaretlerin konum-sınıf bilgilerini veri tabanına aktaran hafif-yapılı bir sistemdir. Çalışmada KCVTS’nin manuel işlem gerektiren geleneksel yöntemlerden, trafik işaretlerinin şekil ve renk gibi özelliklerinin çıkarımına dayanan yarı-geleneksel yöntemlerden ve saha verilerinin merkezdeki güçlü bilgisayarlarda, bilgisayarlı görü ve makine öğrenmesi teknikleri ile işlendiği YZ tabanlı yöntemlerden birçok noktada daha pratik ve verimli olduğu gösterilmiştir.

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Cite This Article

Tatar, C. Ö., Yılmaz, E., Efe, A., Sönmez, B., Özdemir, Y., Danışan, B., Beyaz, H. İ., Yegnidemir, E. (2024). mobilenet based traffic sign detection system for mobile mapping: crowdsourced geographical data collection system. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 39 Sayı: 4, 2305–2315. https://doi.org/10.17341/gazimmfd.1249165

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