Boundary Enhancement and Contrastive Alignment for Unsupervised Domain Adaptive Semantic Segmentation

Abstract

In the actual construction process, the supervision work of concrete pouring has many problems, such as heavy workload, low efficiency, misjudgment and omission. Deep learning shows good performance in computer vision, such as semantic segmentation and object recognition. In this paper, semantic segmentation is used to identify the position of vibrating bar in concrete pouring to provide a basis for detecting whether the vibrating behavior is standardized. Existing semantic segmentation studies ignore whether the edges of objects are finely detected. Recently, contrastive learning has made progress in computer vision. In addition, the training data differs greatly from the actual construction scene, namely domain shift. Therefore, we proposed the domain-adaptation method BECA which consists of two parts: boundary enhancement for accurate detection of edges and contrastive alignment for domain shift. Experiments show that the proposed BECA has unique advantages compared with the previous methods.