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

Year 2024, Volume: 39 Issue: 4, 2305 - 2315, 20.05.2024

Ceren Özcan Tatar , Emrah Yılmaz , Abdullah Efe , Berk Sönmez , Yalçın Özdemir , Burak Danışan , Hale İrem Beyaz , Engin Yegnidemir

https://doi.org/10.17341/gazimmfd.1249165

Abstract

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.

Keywords

kitlesel coğrafi veri toplama sistemi (KCVTS), makine öğrenmesi, trafik işaretleri, mobil haritalama sistemleri, yapay sinir ağları, nesne tespiti

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)

Project Number

1505/3200751 - 2244/119C200

Thanks

Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) 1505 programı 3200751 numaralı proje ve 2244 programı 119C200 numaralı proje ile desteklenmiştir

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

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.

Keywords

Crowdsourced Geographical Data Collection System (CGDCS), machine learning, traffic sign, object detection, mobile mapping systems, artificial neural networks

Project Number

1505/3200751 - 2244/119C200

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