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

Raw Data Is All You Need: Virtual Axle Detector with Enhanced Receptive  Field

Methane is one of the most potent greenhouse gases, and its short atmospheric
half-life makes it a prime target to rapidly curb global warming. However,
current methane emission monitoring techniques primarily rely on approximate
emission factors or self-reporting, which have been shown to often dramatically
underestimate emissions. Although initially designed to monitor surface
properties, satellite multispectral data has recently emerged as a powerful
method to analyze atmospheric content. However, the spectral resolution of
multispectral instruments is poor, and methane measurements are typically very
noisy. Methane data products are also sensitive to absorption by the surface
and other atmospheric gases (water vapor in particular) and therefore provide
noisy maps of potential methane plumes, that typically require extensive human
analysis. Here, we show that the image recognition capabilities of deep
learning methods can be leveraged to automatize the detection of methane leaks
in Sentinel-2 satellite multispectral data, with dramatically reduced false
positive rates compared with state-of-the-art multispectral methane data
products, and without the need for a priori knowledge of potential leak sites.
Our proposed approach paves the way for the automated, high-definition and
high-frequency monitoring of point-source methane emissions across the world.

利用深度学习方法可以自动化检测卫星多光谱数据中的甲烷泄漏，无需事先了解潜在泄漏点，从而大幅降低误报率，为全球点源甲烷排放的自动化、高清晰度和高频率监测铺平了道路。