20.500.12556/RUP-22478 Enhanced precision in axle configuration inference for bridge weigh-in-motion systems using computer vision and deep learning Heavy goods vehicles (HGVs) have a significant impact on road and bridge infrastructure, with overloaded vehicles accelerating structural deterioration and increasing safety risks. Bridge weigh-in-motion (B-WIM) systems estimate gross vehicle weight (GVW) using strain measurements, but inaccuracies in axle configuration recognition can reduce reliability. This study presents a low-cost computer vision (CV) extension for existing B-WIM installations that verifies strain-inferred axle configurations using traffic camera images and flags GVW estimates as reliable or unreliable. Experiments on a data set of over 30,000 HGV records show that by combining convolutional neural networks with strain-based heuristics, GVW reliability can improve from 96.7% to 99.89%, effectively excluding nearly all erroneous measurements. The approach operates without interrupting ongoing B-WIM operations and can be applied retrospectively to historical data. Limitations include the inability to detect raised axles (RAs), which the method excludes as unreliable. This method provides a practical, high-precision enhancement for structural health monitoring of bridges. B-WIM computer vision deep learning B-WIM računalniški vid globoko učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2026-01-16 08:29:02 2026-01-16 08:29:07 2026-01-17 03:05:30 0000-00-00 00:00:00 2025 0 0 str. 6201-6216 iss. 30 Vol. 40 Dec. 2025 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2025-11-16 004.8 1467-8667 10.1111/mice.70144 257515523 RAZ_Soberl_Domen_2025.pdf RAZ_Soberl_Domen_2025.pdf 1 2308C46C30F8F3E31252B28111E45D0E 5a2122780cef04ffae3a688f5a4c11517a9994ddfa0a1cd451a1253debfcf7ac 818cbf26-f2ad-11f0-8639-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=32690 https://onlinelibrary.wiley.com/doi/10.1111/mice.70144 1 0f8a5366-f2ad-11f0-8639-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=32689 Fakulteta za matematiko, naravoslovje in informacijske tehnologije 0 0 0