Enhancing crack detection with convolutional neural networks and oriented non-maximal suppression

Abstract

Detecting of cracks and damages, especially in multi storied buildings is a crucial aspect of infrastructure and building maintenance, as it ensures safety and reliability. An enhanced framework for the crack detection is proposed to identify the fine cracks which are present at greater heights and not captured to the human vision from the ground. The cracks are identified and classified by the deep convolutional neural network model. The Oriented Non-Maximal Suppression module reduces the false positives to improve the classification accuracy and reliability. The proposed method O-CNN(CNN with ONMS)can be used in real-world for the infrastructure inspection and potential applications in civil engineering construction. The ability to input different types of data, including images and videos, makes the proposed system user-friendly and easy to use. Furthermore the system reduces the risk of human error and prevents the huge damages caused to the building. Also, it prevents the major loss which may be caused to the lives. Overall, the proposed system contributes to the field of deep learning and computer vision by providing an effective and better solution for crack detection in real-world scenarios.