Remote-vision-based image processing plays a vital role in the safety helmet and harness monitoring of construction sites, in which computer-vision-based automatic safety helmet and harness monitoring systems have attracted significant attention for practical applications. However, many problems have not been well solved in existing computer-vision-based systems, such as the shortage of safety helmet and harness monitoring datasets and the low accuracy of the detection algorithms. To address these issues, an attribute-knowledge-modeling-based safety helmet and harness monitoring system is constructed in this paper, which elegantly transforms safety state recognition into images’ semantic attribute recognition. Specifically, a novel transformer-based end-to-end network with a self-attention mechanism is proposed to improve attribute recognition performance by making full use of the correlations between image features and semantic attributes, based on which a security recognition system is constructed by integrating detection, tracking, and attribute recognition. Experimental results for safety helmet and harness detection demonstrate that the accuracy and robustness of the proposed transformer-based attribute recognition algorithm obviously outperforms the state-of-the-art algorithms, and the presented system is robust to challenges such as pose variation, occlusion, and a cluttered background.