Detecting semantic regions of construction site images by transfer learning and saliency computation

Effective use of massive construction site images and videos requires an efficient storage and retrieval method. However, significant portions of the image regions contain little useful information to project engineers and managers. To reduce resource waste in data storage and retrieval, we developed a new semantic region detection approach using transfer learning and modified saliency computation method without the need to specify targeted objects. In the new approach, the saliency matrix is generated using labelled bounding boxes, and the semantic regions are selected using a developed algorithm. The proposed method was applied to case studies based on two image datasets. The case studies suggest that the proposed method can efficiently detect semantic regions in site images and detect construction events from other image datasets without a modifying or re-training process. The research contributes to construction image analytics academically by advancing the context-based semantic region detection method and practically by facilitating the effective storage and processing of the massive site images and videos.