Design and practice of smart community management algorithm based on image classification and fire detection

With the acceleration of urbanization and the development of intelligent technology, smart communities, as an important part of modern cities, are gradually becoming a key means to improve the quality of life of residents, optimize resource management and ensure community safety. In order to solve the problems of insufficient image classification accuracy, poor real-time fire detection and weak multi-task collaborative processing ability in smart community management, this study designed and implemented a set of smart community management algorithms based on image classification and fire detection. By optimizing the Convolutional Neural Network (CNN) model, the accuracy of image classification in complex community scenes is improved, and the problems of lighting change and occlusion are solved. At the same time, the module combines deep learning (enhanced YOLOv3) and traditional image processing technology to significantly improve the real-time and accuracy of fire detection.