Open World Object Detection Combining Graph-FPN and Robust Optimization

Abstract

Open world object detection (OWOD) requires detecting all known and unknown object categories in the image, and the model must gradually learn new categories to adaptively update knowledge. Aiming at the problems of low recall rate of unknown objects and catastrophic forgetting of incremental learning in ORE (open world object detection) method, this paper proposes adjustable robust optimization of ORE based on graph feature pyramid (GARO-ORE). Firstly, using the superpixel image structure in Graph-FPN and the hierarchical design of context layer and hierarchical layer, rich semantic information can be obtained and the model can accurately locate unknown object. Then, using the robust optimization method to comprehensively consider the uncertainty, a base class learning strategy based on flat minimum is proposed, which greatly ensures that the model avoids forgetting the previously learnt category knowledge while learning new categories. Finally, the classification weights initiali-zation method based on knowledge transfer is used to improve the adaptability of the model to new classes. Experimental results on the OWOD dataset show that GARO-ORE achieves better detection results on the recall rate of unknown categories. In the three types of incremental object detection tasks of 10 + 10, 15 + 5, and 19 + 1, the mAP is increased by 1.38, 1.42 and 1.44 percentage points, respectively. It can be seen that GARO-ORE can improve the recall rate of unknown object detection, and promote the learning of subsequent tasks while effectively alleviating the catastrophic forgetting problem of old tasks.

Keywords

• open world object detection (owod); graph feature pyramid network; flat minimum; knowledge transfer