Scenarios Engineering for Trustworthy AI: Domain Adaptation Approach for Reidentification With Synthetic Data

Xuan Li, Xiao Wang, Fang Deng, Fei-Yue Wang

Abstract: Reidentification (Re-ID) is a crucial computer vision application with a variety of potential uses in many maritime scenarios, including search, rescue, and surveillance. However, the development of advanced boat reidentification (Boat Re-ID) algorithms necessitates the availability of large-scale Re-ID datasets for model training and evaluation. Inspired by scenarios engineering, this study proposes a new framework for automatically generating a realistic synthetic dataset for boat Re-ID investigation. The synthetic dataset contains 107 boat models and various visual conditions in 36 real backgrounds. The use of synthetic datasets enables the learning-based Re-ID algorithm’s performance to be quantitatively verificated under varying imaging conditions. Nonetheless, our experiments prove that synthetic datasets are inadequate to handle real-world challenges. Therefore, we present a domain adaptation approach that integrates both real and synthetic data to create trustworthy models. This approach employs a multistep training strategy, gradient reversal layer and novel loss functions to preserve the features from two distribution dataset domains. The results of the experiments demonstrate that 1) synthetic datasets can be employed to train boat Re-ID algorithms and quantitatively test the performance of these algorithms under diverse imaging conditions and 2) our approach utilizes the attributes of the two data domains (real and synthetic) to achieve exceptional performance in real-world applications.