Boosting 3D Object Detection with Adversarial Adaptive Data Augmentation Strategy

In real-world applications, autonomous driving systems need to handle a variety of complex scenarios, such as object occlusion and lighting changes. In these scenarios, accurately identifying various objects is crucial for perceiving the surrounding environment and making reliable decisions. In this context, the fusion of Lidar and cameras is vital for the accuracy of object detection. To this end, we propose an adversarial adaptive data augmentation strategy that introduces virtual adversarial perturbations during the image feature extraction process, effectively enhancing the robustness of 3D object detection methods and enabling them to maintain stable performance when facing environmental changes and data perturbations. Experimental results on the nuScenes-mini and KITTI datasets show that, compared with previous 3D object detection methods, our method not only improves detection accuracy but also demonstrates stronger stability.