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 F1 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 F1 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