Crack Detection in Concrete

Original article was published on Deep Learning on Medium

Computer Vision

Crack Detection in Concrete

Using Computer Vision

Introduction

Crack detection is crucial in monitoring the health of infrastructural buildings. As seasons change and the level of moisture remains inconsistent across the year and also due to bad quality of the materials used for construction, cracks usually start to develop in walls of buildings and roads as well. So, before reaching more degraded condition, The foremost signs is of development of cracks on the concrete surface. Beams, Walls, and Roads of concrete are usually stressed which leads to crack which can be formally observed at the microscopic level on the surface. After there minor cracks, Slowly and slowly cracks get broaden due to load and increases its length as well as width. We can conclude that before more damage is induced we can detect the cracks early so that preventive measures can be taken on a serious note leading to fast recovery of the health of the structure.

The determination of factors that are important is the type, number, width and length of the cracks found on the structural surface. This helps determine the degradation level, structural health, and carrying capacity of the concrete structures.

Crack detection can be done by manual human inspection whereby, Testing officials will observe each crack manually measure its dimensions which will help further in calculations of how impactful it will be on the degradation of structural health. The disadvantages of manual human inspection are 1) It takes a lot of time. 2) The human inspection often misses minute cracks that can further broaden. 3) Often misses many cracks that are needed to be monitored. This creates a need for fast, powerful, automatic, and reliable crack detection and analysis strategy. So, Automatic crack detection systems are developed to overcome the slow and traditional human inspection methods.

Basically crack is a visible entity and so image-based crack detection algorithms can be adapted for inspection. Also, some sort of difficulties in the image-based detection procedures are also faced due to the random shape, irregular sizes of crack, and noises in images viz. irregular illuminated conditions, shading, and blemishes. Deep learning algorithms can be applied to solving many challenging problems in image classification. Therefore, Now we conquer this problem of detecting the cracks using image processing methods, deep learning algorithms, and Computer Vision.

Problem is addressed by a simple approach of using image classification with Transfer Learning to classify the images in two categories: Negative (Doesn’t contain Crack) and Positive (Contains Crack).