The invention discloses a tunnel, bridge and
culvert water level detection and alarm
system and method based on
deep learning, and the method comprises the following steps: S1, collecting an image and a
capacitance water level signal at the lowest point of a tunnel or an easy water accumulation region, and generating standardized multi-
source data; s2, inputting the image into a SegmentAnything model, extracting a
water body mask, and calculating an image
water level value in combination with scale mapping; s3, carrying out confidence weighted fusion on the image water level and the
capacitance signal to obtain a fused water level value; s4, constructing a fused water level
time sequence, constructing an SCI Net model, and outputting a predicted water level sequence; s5, generating an alarm
label according to the fused water level and the prediction result; s6,
LED display and acousto-optic response are controlled according to the alarm
label, and an early warning prompt is started when a serious alarm is given; s7, capturing an image in an early warning or serious
alarm state, and generating an alarm data packet; and S8, uploading to an
urban water level monitoring platform through
the Internet of Things module. According to the invention, intelligent identification and remote early warning of the
ponding state of the tunnel bridge and
culvert are realized.