Lightweight Vehicle Damage Detection With NMS‐Free YOLOv10s and AKConv

This study addresses the inefficiency and human error susceptibility of traditional vehicle damage assessment methods by proposing an intelligent detection model based on an enhanced YOLOv10s algorithm. The model incorporates an adaptive Kernel convolution (AKConv) module to replace standard convolutions, strengthening its capability to characterise multi‐scale and irregular damage features. Leveraging YOLOv10s' end‐to‐end training mechanism that eliminates non‐maximum suppression requirements, computational complexity is reduced. Experimental validation on a 14‐category vehicle damage dataset shows the optimised YOLOv10s‐AKConv model achieves approximately 6% lower FLOPs and 2% fewer parameters while maintaining lightweight properties. The model attains 81.48% mAP50‐95 on the test set—a 1% improvement over the baseline‐ and demonstrates exceptional robustness in extreme conditions. These advancements significantly enhance damage recognition accuracy and efficiency, establishing a viable technical approach for practical applications such as insurance claim assessment.