Accepted Papers


SURFACE CRACK DETECTION USING HIERARCHAL CONVOLUTIONAL NEURAL NETWORKS

Davis Bonsu Agyemang and Mohamed Bader-El-Den
The School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE.

ABSTRACT

Cracks on concrete walls can imply that a building possesses issues with its structural integrity. Surveyors who inspect these defects are expected to provide their customers with excellent evaluation regarding its severity. The process is currently conducted through visual inspection, resulting in occasions of subjective judgements being made on the classification and severity of the concrete crack which poses danger for customers and the environment as it not being analysed objectively. Many researchers have applied numerous classification techniques to tackle this issue but from the author's knowledge, their methods do not provide the severity levels of concrete cracks and have no feedback mechanism for adaptability of when their method classifies incorrectly. In this paper, the author proposes in building a mobile application with the 2 mentioned capabilities and using a trained Hierarchal-Convolutional Neural Network(H-CNN) to evaluate the concrete surface via images taken via the mobile device.

KEYWORDS

Concrete cracks,classification algorithms, Convolutional Neural Networks(CNN) ,Hierarchical Convolutional Neural Network(H-CNN), accuracy,loss,Python,Flask, HTM,CSS &Javascript.