With the acceleration of global economic development and urbanization, the number of vehicles has been increasing year by year, especially in congested urban environments, where the risk of vehicle damage has increased significantly. Vehicle damage detection has become the key to auto insurance claims, intelligent vehicle management and maintenance efficiency improvement. Traditional manual detection methods have low efficiency and high cost, so it is hard to satisfy the requirement of intelligence management. To this end, this research introduces a car damage detection method based on an improved YOLOv11 algorithm, called CAA-YOLOv11, on the CarDamageDetection datasets using three key improvements: first, the Context Guided Block module (CG block) is used to replace the traditional convolutional layer to elevate the feature extraction competence; second, the AIFI module from RT-DETR is incorporated to replace the SPPF component in YOLOv11, thereby enhancing the model's detection capabilities in complex scenarios through the application of the self-attention mechanism; finally, the ASFFHead module is integrated into YOLOv11 to optimize the detection head and improve the scale invariance and detection accuracy by adaptive fusion of features at different scales. Experimental findings demonstrate that CAA-YOLOv11 substantially boosts the overall performance of YOLOv11, which provides strong technical support for intelligent vehicle management and automated auto insurance claims.
Improved YOLOv11 algorithm for vehicle damage detection
International Conference on Image Processing and Deep Learning (IPDL 2025) ; 2025 ; Chengdu, China
Proc. SPIE ; 13707 ; 137070K
2025-07-30
Conference paper
Electronic Resource
English
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