Hossain, Muhammad IqbalDofadar, Dibyo FabianYusuf, LamiaZahin, FaiazSultana, SumaiyaAslam, Mohammad Ishmam BinKadir, Ahmed Tahlil2025-12-302025-12-3020252025-10ID 21201098ID 21101090ID 21201223ID 21201041ID 21101138http://hdl.handle.net/10361/27392Cataloged from PDF version of thesis.Includes bibliographical references (pages 61-63).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Environmental, biological, and structural reasons are causing cultural heritage sites in Bangladesh to deteriorate more quickly. At the same time, traditional manual inspection procedures are still time-consuming, subjective, and hard to scale. This study introduces an automated deep learning framework for the detection and classification of surface flaws in historical structures, utilizing the Historic Place Dataset including 2,292 photos from Panam City, a UNESCO World Heritage Site in Sonargaon, Bangladesh. We use Weighted Boxes Fusion (WBF) to combine YOLOv8- small for speed and Faster R-CNN for accuracy to find five types of damage: Artistic, Corroded brick, Corroded plaster, Fungus, and Living plant elements. To address the class imbalance and limited data, we use hybrid augmentation methodologies that use both online and offline methods. The experimental results demonstrate that Faster R-CNN has the best test-set performance (mAP@0.5 = 0.8945), and the WBF ensemble (mAP@0.5 = 0.8898) does much better than YOLOv8 alone (mAP@0.5 = 0.864). Per-class analysis shows that features that are easy to tell apart get better results, but damage types that are hard to tell apart are still hard to work with. This study shows that deep learning methods can work even when there isn’t a lot of data available, and it also shows how to improve them through systematic augmentation and architectural optimization.74 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Heritage defect detectionDeep learningR-CNNYOLOv8Heritage preservationHistoric building monitoringData augmentationEnsemble learningAutomated damage assessmentObject detectionNeural networks (Computer science).Cultural property--Protection.Historic buildings--Maintenance and repair.Deep learning (Machine learning).Computer vision.Sonāra Gām̐ (Bangladesh).Historic buildings--Damages--Identification.Historic buildings--Conservation and restoration.Historic preservation.Structural health monitoring.Thesis