Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection

Umme Hafsa Billah, Hung Manh La, Alireza Tavakkoli

Sensors 2020 article cited by 27

Source Open-access PDF More in Engineering & Materials

importance 5/10 ai assisted Infrastructure Maintenance and Monitoring infrastructure inspection stage: research

Problem

Deteriorated concrete surfaces cause hazardous incidents, so automated crack inspection is needed instead of manual checks.

Why it matters

Undetected cracks in concrete structures can lead to failures endangering public safety.

What the AI did

A deep convolutional neural network performs crack segmentation, with a feature silencing module that removes non-discriminative feature maps to improve robustness, sensitivity and specificity.

Results

No concrete numbers; abstract reports experimental results showing improved robustness, sensitivity and specificity

Impact

Experiments supported the framework's benefit for automated concrete crack detection.

Record

Venue	Sensors
Published	2020
Work type	article
Authors	Umme Hafsa Billah, Hung Manh La, Alireza Tavakkoli
Citations	27 (OpenAlex, at import time)
References	65
Model or method	Deep convolutional neural network with feature silencing module (no named architecture)
Organization	not stated in source
Primary source	https://doi.org/10.3390/s20164403
Open access	gold
Keywords	crack detection, deep learning, CNN, segmentation, infrastructure inspection

Importance rating

5 / 10

real but narrower advance

Automated concrete crack inspection addresses public-safety hazards from deteriorating structures; abstract claims safety relevance but quantifies no scale or results.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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