POTHOLE DETECTION SYSTEM USING MACHINE LEARNING ON MOBILE DEVICE / (Kayıt no. 292961)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03358nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20250109090220.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240927d2024 cy o|||| |||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 3467 |
| Cutter no | P48 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Peter, Erikpara Gbesimi |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | POTHOLE DETECTION SYSTEM USING MACHINE LEARNING ON MOBILE DEVICE / |
| Sorumluluk Bildirimi | ERIKPARA GBESIMI PETER ; SUPERVISOR, PROF. DR. ERBUĞ ÇELEBİ |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 83 sheets ; |
| Boyutları | 30 cm |
| Birlikteki Materyal | +1 CD ROM |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering |
| 520 ## - ÖZET NOTU | |
| Özet notu | As vehicular traffic continues to increase globally, the condition of road surfaces has become a significant concern for both infrastructure integrity and public safety. Traditional road maintenance methods, often reliant on manual inspections, are labor-intensive and insufficiently comprehensive. This research addresses the urgent need for an effective solution to monitor and assess road damage through advanced technological methods. By focusing on the problem of detecting road defects like potholes and cracks, the study aims to develop a more efficient and accurate system for road condition assessment.<br/>To tackle this issue, the research employs a machine learning-based approach, specifically leveraging the YOLOv8n (You Only Look Once version 8n) object detection model for automatic road defect detection. The methodology includes collecting high-resolution images of road surfaces, preprocessing these images for optimal input, and training a Convolutional Neural Network (CNN) to identify and categorize various types of road damage. Additionally, the study explores the implementation of this model on mobile devices to assess its feasibility for real-time, on-site road condition evaluations.<br/>The experimental results reveal that the YOLOv8n model achieves a precision of 0.625 and a recall of 0.582, with an mAP50 score of 0.598 and an mAP50-95 score of 0.321. Performance evaluations indicate that the model can process images with an average inference time of 3.2 milliseconds and a total image processing time of approximately 7.4 milliseconds. Furthermore, the research explores the impact of varying the number of threads used in the mobile app, demonstrating that increasing the number of threads from 2 to 4 significantly reduces inference time, thus enhancing the efficiency of the detection system.<br/>In conclusion, this study successfully demonstrates the potential of machine learning techniques for road damage detection and the feasibility of implementing these methods on mobile devices for real-time applications. The findings highlight the effectiveness of YOLOv8n in detecting road defects and provide insights into optimizing system performance through threading strategies. Future work could explore further improvements in model accuracy, scalability for diverse road<br/>iii<br/>conditions, and integration with automated maintenance systems to better support road management efforts. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Computer Engineering |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Çelebi, Erbuğ |
| İlişkili Terim | supervısor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 16.10.2024 | Bağış | YL 3467 P48 2024 | T3914 | 16.10.2024 | C.1 | 16.10.2024 | Thesis | Computer Engineering | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 16.10.2024 | Bağış | YL 3467 P48 2024 | CDT3914 | 16.10.2024 | C.1 | 16.10.2024 | Suppl. CD | Computer Engineering |