Computer Vision Powered Web Application to Identify Brick Fractures
Computer Vision Powered Web Application to Identify Brick Fractures

Summary/Abstract: The objective of this study is to improve fracture identification in bricks at a factory located in Virú through the use of a computer vision application during the year 2025. The study employed a pure experimental design. The population consisted of all brick quality inspection processes carried out by construction companies, with a sample size of 30 processes. The data collection technique employed was direct observation, and the instrument used was an observation form. The computer vision application was developed using Python 3.12 and the Flask framework, with MongoDB as the database management system and Mobile-D as the software development methodology. Descriptive analysis was performed using Microsoft Excel 2019, while inferential analysis was conducted using Jamovi 2.6.44. The results showed a 32.91% increase in fracture identification, a reduction of 114.4 seconds in the average time to identify brick fractures, and a 30.6% improvement in identification accuracy. In conclusion, the use of a computer vision application significantly improves fracture identification in brick factories.

• Details
• Contents