Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Saseekala, M.; Thomas, Atulya Title A name given to the resource Smart AI Tool for Accident Damage Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1122;pp.399-409 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7426-5_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7426-5_31</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218180686?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218180686?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Saseekala M., Faculty of Computer Applications, Business Analytics Specialization, School of Business and Management, Christ University, Bangalore, India; Thomas A., Christ University, Bangalore, India Description An account of the resource Accidents and fatalities from motor vehicle accidents are major concerns despite substantial advancements in safety technology. Because of this, the industry has made significant investments in creating new safety features, like cutting-edge driver assistance systems, and raising public awareness of safe driving habits. In general, car accidents can result in severe damage to the vehicles involved, and assessing and repairing that damage can be time-consuming and expensive. Manual inspection of vehicles is prone to errors and often requires trained professionals to identify the extent and location of the damage. Therefore, there is a need to develop an automated system that can detect and assess the damage caused to vehicles using AI and deep learning techniques. An image-based processing technique, YOLOv3, is proposed in this work to automate damage detection on automobiles. In the work, we used CNN to create a Mask R-convolutional neural Networks model to identify the location of damage on a car. The damaged area is precisely marked in the images. The base weights from the Mask R-CNN COCO dataset are used to train the model. 21 epochs are used to process the images. The surface of the damage is highlighted in the final image using a color splash technique after processing. Auto insurance firms, vehicle rental companies, and repair shops would all benefit from this automated method of determining the degree of exterior vehicle damage and then calculating the severity of that damage. The value of fraudulent auto insurance claims can also be reduced. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial intelligence; Deep learning; Mask R-CNN; SVM; VGG model Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977425-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online