Multi-stage context-guided semantic segmentation in construction sites

Semantic segmentation refers to the segmentation of different objects within images to obtain a semantic interpretation of each pixel position based on semantic information. With the development of artificial intelligence and computer vision, intelligent visual surveillance solutions for construction sites are playing an increasingly prominent role in site monitoring. For this purpose, we propose a semantic segmentation network for intelligent scene perception in construction sites. The features are extracted with the residual block and channel-wise feature aggregation module, thus obtaining multi-scale features with rich information. Then, high-level futures are further enhanced with long-range contextual information, which is adopted as guidance for the decoding process. Experimental results show that the proposed network can efficiently process construction site scenes in real-time and has important practical applications.