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การตรวจจับและรู้จำความเสียหายของรถยนต์ โดยใช้การเรียนรู้เชิงลึก
Car damage detection and recognition using deep learning
@คณะเทคโนโลยีสารสนเทศ
#KLLC 2024
#Industry 4.0
รายละเอียด
This senior project aimed to develop an automated system for car damage detection, segmentation, and captioning. The project evaluated the performance of the YOLOv8 model for damage detection and explored pseudo-labeling methods using deep spectral methods to generate pseudo-labeling and find approach effectiveness pseudo-label for image segmentation to overcome limited labeled data. The results showed promising outcomes, with improvements in mean IoU for certain damage classes. The project also developed language models for damage captioning, generating relevant descriptions for damaged cars. Although there is room for further improvement, the project provided insights into model performance and demonstrated the potential for cost-effective pseudo-labeling and enhanced damage evaluation processes in the car insurance sector.
วัตถุประสงค์
Currently, the automobile insurance industry is a business that is growing in response to the demand for car usage. In the automobile insurance sector, there are services that need to be performed both before and after policy issuance. Examples include assessing the value of damaged car parts for policyholders who have experienced damage and evaluating the condition of a car before it is insured. From the given example of services provided by the automobile insurance business, it is evident that these issues are complex and require knowledge and experience. Therefore, it is essential to have experts involved in damage assessment or evaluating the claim value of damaged parts. Accurately describing car damages is crucial in the automotive industry as it helps assess re- pairs and calculate insurance claims. Currently, this task is carried out by trained experts who have undergone specialized training. However, it can be time-consuming and prone to errors With advancements in technology, many industries are seeking to leverage deep learning mod- els to create business opportunities. However, one limitation is the requirement for large labeled datasets, which can be time-consuming and labor-intensive to create. This high cost in terms of time and resources makes it challenging to apply deep learning models to address business problems due to data limitations. The researchers are interested in developing an automated system that utilizes deep learning tech- niques to detect and recognize car damage in images. This system aims to support the automobile insurance industry in evaluating car conditions prior to insurance coverage and assessing damages to car parts for claim processing. The objective is to streamline the assessment process, minimize er- rors, and enhance the accuracy of damage evaluation. Additionally, the researchers intend to utilize language-based models to describe the types of damages and the affected parts, providing explana- tions. They also aim to devise an effective approach for generating pseudo-labels as a solution to the limitations posed by insufficient labeled data.
ผู้จัดทำ
นนทพัทธ์ เทศปัญ
NONTHAPAHT TASPAN
#นักศึกษา
สมาชิก
บุคอรีย์ หมาดทิ้ง
BUKORREE MADTHING
#นักศึกษา
สมาชิก
กิติ์สุชาต พสุภา
Kitsuchart Pasupa
#อาจารย์
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