Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach

Abstract

Recently, glass crack detection methods have been emerging in Artificial intelligence programming. The early detection of the crack in glass could save many lives. Glass fractures can be detected automatically using machine vision. However, this has not been extensively researched. As a result, a detection algorithm is a benefit to study the mechanics of glass cracking. To test the algorithm, benchmark data are used and analysed. According to the first findings, the algorithm is capable of figuring out the screen more or less correctly and identifying the main fracture structures with sufficient efficiency required for majority of the applications. This research article has addressed the early detection of glass cracks by using edge detection, which delivers excellent accuracy in fracture identification. Following the pre-processing stage, the CNN technique extracts additional characteristics from the input pictures that have been provided due to dense feature extraction. The "Adam" optimizer is used to update the bias weights of networks in a cost-effective manner. Early identification is achievable with high accuracy metrics when using these approaches, as shown in the findings and discussion part of this paper.