Algorithm Evolution and Application Strategy of Deep Learning in Violation Recognition at Homework Sites

In the industrial and construction fields, safety supervision at work sites has always been a major challenge. With the development of deep learning technology, its application in identifying and monitoring illegal behaviors on the job site has become a hot research topic. This study focuses on the application of deep learning technology in job sites. Firstly, by analyzing existing safety monitoring models, the differences and connections between deep learning algorithms and traditional monitoring methods are clarified. The study focuses on the application effectiveness and technical challenges of deep learning models in identifying violations in job sites. In terms of methodology, this article constructs a multi class on-site landmark recognition system and uses deep learning models such as Convolutional Neural Network (CNN) to train and learn from video and image data at the job site, achieving automatic recognition and classification of violations. As the best performing scenario, the construction site demonstrated the excellent performance of the model in this environment, with an accuracy rate of 92% and an F1 score of 89%, indicating that the model can effectively identify violations in this scenario. This study fills the research gap of deep learning in practical security monitoring applications and has important theoretical and practical value for promoting the development of intelligent security monitoring systems.