HL-YOLO: Improving Vehicle Damage Detection with Heterogeneous Convolutions and Large-Kernel Attention
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| Publicado en: | World Electric Vehicle Journal vol. 16, no. 12 (2025), p. 640-660 |
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| Autor Principal: | |
| Outros autores: | , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3286358120 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2032-6653 | ||
| 024 | 7 | |a 10.3390/wevj16120640 |2 doi | |
| 035 | |a 3286358120 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Li, Weijun | |
| 245 | 1 | |a HL-YOLO: Improving Vehicle Damage Detection with Heterogeneous Convolutions and Large-Kernel Attention | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Inclusion | ||
| 653 | |a Computer vision | ||
| 653 | |a Insurance | ||
| 653 | |a Neural networks | ||
| 653 | |a Effectiveness | ||
| 653 | |a Damage detection | ||
| 653 | |a Design | ||
| 653 | |a Architecture | ||
| 653 | |a Damage assessment | ||
| 653 | |a Complexity | ||
| 653 | |a Insurance claims | ||
| 653 | |a Object recognition | ||
| 653 | |a Localization | ||
| 653 | |a Real time | ||
| 653 | |a Intelligent transportation systems | ||
| 653 | |a Semantics | ||
| 653 | |a Vehicles | ||
| 700 | 1 | |a Xie Huawei | |
| 700 | 1 | |a Lin Peiteng | |
| 700 | 1 | |a Huang, Liyan | |
| 773 | 0 | |t World Electric Vehicle Journal |g vol. 16, no. 12 (2025), p. 640-660 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286358120/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286358120/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286358120/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |