Rising maintenance costs of ageing infrastructure necessitate innovative monitoring techniques. This paper presents a new approach for axle detection, enabling real-time application of Bridge Weigh-In-Motion (BWIM) systems without dedicated axle detectors. The proposed method adapts the Virtual Axle Detector (VAD) model to handle raw acceleration data, which allows the receptive field to be increased. The proposed Virtual Axle Detector with Enhanced Receptive field (VADER) improves the \(F_1\) score by 73\% and spatial accuracy by 39\%, while cutting computational and memory costs by 99\% compared to the state-of-the-art VAD. VADER reaches a \(F_1\) score of 99.4\% and a spatial error of 4.13~cm when using a representative training set and functional sensors. We also introduce a novel receptive field (RF) rule for an object-size driven design of Convolutional Neural Network (CNN) architectures. Based on this rule, our results suggest that models using raw data could achieve better performance than those using spectrograms, offering a compelling reason to consider raw data as input.

该研究提出了一种新的轴检测方法，可以实时应用桥梁称重系统(BWIM)而无需专用轴检测器，通过使用虚拟轴探测器(VAD)模型处理原始加速度数据，提高了感受野，改进了F1得分和空间准确度，同时大幅降低了计算和存储成本，并证明了使用原始数据作为输入可能比使用频谱图表现更好。

原始数据无所不能：具有增强感受野的虚拟轴线探测器