SADANet: Integrating Scale-Aware and Domain Adaptive for Traffic Sign Detection

It holds great implications for large-scale traffic scene understanding, to enable traffic sign detection in real environment. The large intra-category variance in features caused by instances of different spatial scales and appearances are the main problem. In this paper, a traffic sign detection framework using scale-aware and domain adaptive network (SADANet) was proposed, which seamlessly combines a multiscale prediction network (MSPN) with a domain adaptive network (DAN) in a tightly-coupled manner in order to tackle the challenge. Specifically, MSPN is dedicated to extracting the multiscale feature. It utilizes fully, the low-level location and high-level semantic information, and considers the combination of the context information and the instance specific content awareness in the scale transformation. DAN is dedicated to making features domain invariant without enough labeled test data. It aligns the domain distributions from the different scales effectively by leveraging the mapping relationship between the image representation and the multiscale features. Experimental results show that the SADANet is effective in traffic sign detection task and is also competitive when compared with the state-of-the-art methods.