Prominent Edge Characteristics for Open World Object Detection

Open world object detection aims to simulate the human process of recognizing objects in real life scenarios. It can identify categories not introduced during the training phase as unknown during the testing phase and recognize these unknowns in subsequent incremental learning. This capability is beyond the reach of traditional object detection, making it closer to real world human experiences and thus garnering significant attention from researchers. However, existing studies largely focus on generating better pseudo labels to improve the recall rate of unknown objects, which evidently neglects the intrinsic differences between objects and backgrounds. Considering important information such as the visual saliency and edge features of objects in realistic scenes, this paper proposes a novel solution called PEC, which directly extracts objects from their background to detect known and unknown objects more accurately. Comprehensive experiments on the M-OWODB benchmark demonstrate that our method outperforms other current mainstream approaches.