HQA‐Trans: An end‐to‐end high‐quality‐awareness image translation framework for unsupervised cross‐domain pedestrian detection

Abstract Unsupervised cross‐domain pedestrian detection has attracted attention in recent years. Although some works adopted unsupervised image translation frameworks to generate an intermediate domain to narrow the gap between source and target domains, the images in the intermediate domain tend to be distorted due to the instability of the generation network. In this work, we propose a new framework to improve the image quality of the generated intermediate domain via an end‐to‐end translation framework. First, an image quality assessment index is adopted and adjusted appropriately. The part that controls the image quality is kept, and the part that adversely affects the domain style translation is discarded. Secondly, the adjusted image quality assessment index is integrated into the unsupervised image translation framework, where a new loss with the index's weight is proposed. An end‐to‐end high‐quality‐awareness image translation framework is constructed to generate a high‐quality intermediate domain directly through this process. Finally, the intermediate domain with high‐quality images is applied for cross‐domain pedestrian detection. Experimental results on benchmark datasets show that the proposed framework can effectively improve unsupervised cross‐domain pedestrian detection performance. Compared with some state‐of‐the‐art works, the proposed framework can also achieve superior performance under miss rate metrics.