AEAM3D: Adverse Environment-Adaptive Monocular 3D Object Detection via Feature Extraction Regularization

Yixin Lei, Xingyuan Li, Zhiying Jiang, Xinrui Ju, Jinyuan Liu

Abstract: 3D object detection plays a crucial role in intelligent vision systems. Detection in the open world inevitably encounters various adverse scenes while most of existing methods fail in these scenes. To address this issue, this paper proposes a monocular 3D detection model, termed AEAM3D, which effectively mitigates the degradation of detection performance in various harsh environments. Additionally, we assemble a new adverse 3D object detection dataset encompassing some challenging scenes, including rainy, foggy, and low light weather conditions. Experimental results demonstrate that our proposed method outperforms current state-of-the-art approaches by an average of 3.12% in terms of AP R40 for car category across adverse environments.