  Rising maintenance costs of ageing infrastructure necessitate innovative
monitoring techniques. This paper presents a new approach for detecting axles,
enabling real-time application of Bridge Weigh-In-Motion (BWIM) systems without
dedicated axle detectors. The proposed Virtual Axle Detector with Enhanced
Receptive Field (VADER) is independent of bridge type and sensor placement
while only using raw acceleration data as input. By using raw data instead of
spectograms as input, the receptive field can be enhanced without increasing
the number of parameters. We also introduce a novel receptive field (RF) rule
for an object-size driven design of Convolutional Neural Network (CNN)
architectures. We were able to show, that the RF rule has the potential to
bridge the gap between physical boundary conditions and deep learning model
development. Based on the RF 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. The proposed VADER achieves to
detect 99.9 % of axles with a spatial error of 4.13 cm using only acceleration
measurements, while cutting computational and memory costs by 99 % compared to
the state-of-the-art using spectograms.
