POTHOLE DETECTION AND REPORTING SYSTEM USING IMAGE PROCESSING / (Kayıt no. 293109)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 02933nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20250109100756.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240927d2024 cy d|||| |||| 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 3563 |
| Cutter no | A59 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Anyaegbuna, Ikenna Emmanuel |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | POTHOLE DETECTION AND REPORTING SYSTEM USING IMAGE PROCESSING / |
| Sorumluluk Bildirimi | IKENNA EMMANUEL ANYAEGBUNA ; SUPERVISOR, ASSOC. PROF. DR. UMAR ÖZGÜNALP |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 77 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 Electronics and Communication Engineering |
| 520 ## - ÖZET NOTU | |
| Özet notu | Road damage detection is a pivotal aspect of infrastructure upkeep ensuring the safety and efficiency of transportation networks. Current methods often fall short in precisely categorizing distinct types of road surface irregularities and restricting maintenance strategies. This research aims to revolutionize road damage detection by leveraging cutting-edge computer vision techniques, specifically employing YOLOv8 models—YOLOv8n, YOLOv8s and YOLOv8m.<br/>Methodologies encompass the rigorous training and optimization of these models using a comprehensive dataset sourced from the "2020 IEEE International Conference on Big Data." The primary objective is to enable these systems to detect and categorize various types of road surface damages accurately. Performance evaluations and comparative analysis among the YOLOv8 variants form the core of the study to determine their applicability in practical scenarios.<br/>YOLOv8n demonstrated moderate performance with reasonable precision and recall rates albeit displaying relatively lower mAP50 and mAP50-95 scores across diverse damage classes. Conversely, YOLOv8s showcased amplified precision, recall and mAP scores signifying superior object detection capabilities compared to prior models. Nonetheless, YOLOv8m exhibited noteworthy advancements in precision and recall albeit at the cost of increased computational demands.<br/>In conclusion, while YOLOv8 models prove effective in detecting and categorizing road surface damages their suitability varies based on computational requirements. YOLOv8n is fitting for devices with limited computational resources, YOLOv8s improves accuracy for moderately capable devices and YOLOv8m, although accurate demands higher computational power. This study offers crucial insights for selecting appropriate YOLOv8 models tailored to diverse computational scenarios thereby contributing to advancements in road infrastructure maintenance and safety. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Electronics and Communication Engineering |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Özgünalp, Umar |
| İlişkili Terim | supervisor |
| 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 | 21.11.2024 | Bağış | YL 3563 A59 2024 | T4010 | 21.11.2024 | C.1 | 21.11.2024 | Thesis | Electronics and Communication Engineering | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 21.11.2024 | Bağış | YL 3563 A59 2024 | CDT4010 | 21.11.2024 | C.1 | 21.11.2024 | Suppl. CD | Electronics and Communication Engineering |