Cross-Domain Object Detection Based on Attention Mechanism

A sizable training dataset is always necessary to ensure the robustness of object detection algorithms. However, the number of training datasets is limited by the shortage of real sample data and the high cost of labeling, so we want to improve the cross-domain performance of object detection from virtual to real-world domains. In this work, we use Faster R-CNN as the basic model and supplement it with an attention mechanism. It then confirms the effectiveness of the attention module in cross-domain object detection tasks by comparison. Additionally, to further enhance algorithm performance, we create a new attention model based on the structure of the classical attention model, develop several optimization strategies, and evaluate each one through experiments to identify the best model. This model is preferable to the existing attention model cited in this work since it has fewer parameters, a similar computational load, and higher accuracy. It also offers a novel attention-assisted model for research on cross-domain object detection algorithms.