000			02933nam a22002657a 4500
003			KOHA
005			20250109100756.0
008			240927d2024 cy d|||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3563 _bA59 2024
100	1		_aAnyaegbuna, Ikenna Emmanuel
245	1	0	_aPOTHOLE DETECTION AND REPORTING SYSTEM USING IMAGE PROCESSING / _cIKENNA EMMANUEL ANYAEGBUNA ; SUPERVISOR, ASSOC. PROF. DR. UMAR ÖZGÜNALP
264			_c2024
300			_a77 sheets ; _c30 cm _e+1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electronics and Communication Engineering
520			_aRoad 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. 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. 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. 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	_aElectronics and Communication Engineering _vDissertations, Academic
700	1		_aÖzgünalp, Umar _esupervisor
942			_2ddc _cTS
999			_c293109 _d293109