Identifying Objects at Construction Sites Using CNN

The construction industry generates a massive number of images throughout a project’s lifecycle, including design, construction, and post-construction phases. However, managing and organizing these images can be a challenging task due to their large volume and the need to identify and label the various objects and materials captured in each image. This project aims to develop a system for identifying and labelling images captured at a construction site. The system utilizes object detection and deep neural network (CNN) to accurately identify different objects and materials present in the images, such as excavator, dump truck, concrete, and forklift. The identified objects are then labelled with descriptive tags to facilitate easy search and retrieval of the images later. By automating the identification and labelling of objects in images, the system can improve project management, enhance safety and security, and reduce errors and delays associated with manual image labelling and organization.