HL-YOLO: Improving Vehicle Damage Detection with Heterogeneous Convolutions and Large-Kernel Attention

Abstract

Accurate vehicle damage detection is essential in intelligent transportation systems, insurance claim assessment, and automotive maintenance. Although conventional detection models demonstrate strong performance, they still struggle to capture fine-grained details and long-range dependencies, which can constrain their effectiveness in real-world applications. To address these limitations, we propose HL-YOLO, an enhanced YOLO11-based architecture that integrates Heterogeneous Convolutions (HetConv) to improve feature extraction diversity and Large-Kernel Attention (LSKA) to strengthen contextual representation. Model evaluation results on a vehicle damage dataset demonstrate that HL-YOLO consistently outperforms the YOLO11 baseline, achieving relative improvements of 2.5% in precision, 5.8% in recall, 3.9% in mAP50, and 3.1% in mAP50–95. These results underscore the model’s robustness in identifying complex damage types, ranging from scratches and dents to accident-induced damage. Although inference latency increased moderately due to the added architectural complexity, the overall accuracy gains confirm the effectiveness of HL-YOLO in scenarios where detection reliability is prioritized over real-time speed. The proposed model shows strong potential for deployment in insurance automation, intelligent traffic monitoring, and vehicle after-service systems, providing a reliable framework for accurate vehicle damage assessment.