<p>The severity of vehicle damage is very important in determining road safety, insurance claim and automated vehicle inspection. Although the recent methods of computer vision were promising in the localization of damaged areas, it is difficult to discriminate the level of damage precisely because of subtle visual variations, class disparity, and irregular damage images. In a bid to solve these problems, this paper suggests a severity-sensitive YOLOv8-based instance segmentation system, which incorporates curriculum learning, Harris Hawks Optimization (HHO) to hyperparameter optimization, and confidence thresholding at the per-class level. The proposed learning strategy in the curriculum allows the training to be progressive, whereby initial training occurs with understanding of types of coarse damage, and then further training is given in fine-grained severity discrimination. HHO is used to automatically tune important hyperparameters with severity-sensitive fitness objectives to enhance the robustness of segmentation without changing the architecture. Also, class-specific confidence thresholds are proposed to adjust the precision and recalling to the levels of severity. Experimental analysis of a curated vehicle damage severity dataset shows that convergence is stable and the performance is competitive, with Box mAP50 of 0.271 and Mask mAP50 of 0.135, showing the best performance at an early training phase. The proposed lightweight YOLOv8s-Seg model has shown to be better than a larger YOLOv8m-Seg baseline with practical thresholds of IoU, which proves its applicability in practice. The confusion matrix and class-wise analysis indicate the presence of a strong detection of severe damages, whereas surface defects that emerge in subtle cases are noted as a challenge. In general, the proposed framework offers an effective, understandable, and implementable algorithm of automated damage severity in vehicles.</p>