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Smart Car Damage Assessment Using Enhanced YOLO Algorithm and Image Processing Techniques

Wedi'i Gadw mewn:
Cyhoeddwyd yn:Information vol. 16, no. 3 (2025), p. 211
Prif Awdur: Muhammad Remzy Syah Ramazhan
Awduron Eraill: Alhadi Bustamam, Rinaldi, Anwar Buyung
Cyhoeddwyd:
MDPI AG
Pynciau:
Accuracy
Deep learning
Artificial intelligence
Computer vision
Neural networks
Smart cars
Classification
Damage detection
Algorithms
Damage assessment
Boxes
Automation
Object recognition
Localization
Image processing
Internet of Things
Efficiency
Insurance industry
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Crynodeb:Conventional inspections in car damage assessments depend on visual judgments by human inspectors, which are labor-intensive and prone to fraudulent practices through manipulating damages. Recent advancements in artificial intelligence have given rise to a state-of-the-art object detection algorithm, the You Only Look Once algorithm (YOLO), that sets a new standard in smart and automated damage assessment. This study proposes an enhanced YOLOv9 network tailored to detect six types of car damage. The enhancements include the convolutional block attention module (CBAM), applied to the backbone layer to enhance the model’s ability to focus on key damaged regions, and the SCYLLA-IoU (SIoU) loss function, introduced for bounding box regression. To be able to assess the damage severity comprehensively, we propose a novel formula named damage severity index (DSI) for quantifying damage severity directly from images, integrating multiple factors such as the number of detected damages, the ratio of damage to the image size, object detection confidence, and the type of damage. Experimental results on the CarDD dataset show that the proposed model outperforms state-of-the-art YOLO algorithms by 1.75% and that the proposed DSI demonstrates intuitive assessment of damage severity with numbers, aiding repair decisions.
ISSN:2078-2489
DOI:10.3390/info16030211
Ffynhonnell:Advanced Technologies & Aerospace Database