Robust object detection for autonomous driving based on semi-supervised learning  

作　　者：Wenwen Chen Jun Yan Weiquan Huang Wancheng Ge Huaping Liu Huilin Yin 

机构地区：[1]College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China [2]School of Electrical Engineering and Computer Science,Oregon State University,Corvallis 97331-3211,USA

出　　处：《Security and Safety》2024年第4期18-43,共26页一体化安全(英文)

基　　金：supported by the National Natural Science Foundation of China under Grant No. 61701348 and No. 62133011

摘　　要：Deep learning based on labeled data has brought massive success in computer vision, speech recognition, and natural language processing. Nevertheless, labeled data is just a drop in the ocean compared with unlabeled data. How can people utilize the unlabeled data efectively? Research has focused on unsupervised and semi-supervised learning to solve such a problem. Some theoretical and empirical studies have proved that unlabeled data can help boost the generalization ability and robustness under adversarial attacks. However, current theoretical research on the relationship between robustness and unlabeled data limits its scope to toy datasets. Meanwhile, the visual models in autonomous driving need a significant improvement in robustness to guarantee security and safety. This paper proposes a semi-supervised learning framework for object detection in autonomous vehicles, improving the robustness with unlabeled data. Firstly, we build a baseline with the transfer learning of an unsupervised contrastive learning method—Momentum Contrast(MoCo). Secondly,we propose a semi-supervised co-training method to label the unlabeled data for retraining,which improves generalization on the autonomous driving dataset. Thirdly, we apply the unsupervised Bounding Box data augmentation(BBAug) method based on a search algorithm, which uses reinforcement learning to improve the robustness of object detection for autonomous driving. We present an empirical study on the KITTI dataset with diverse adversarial attack methods. Our proposed method realizes the state-of-the-art generalization and robustness under white-box attacks(DPatch and Contextual Patch) and black-box attacks(Gaussian noise, Rain, Fog, and so on). Our proposed method and empirical study show that using more unlabeled data benefits the robustness of perception systems in autonomous driving.

关 键 词：Adversarial attack ROBUSTNESS autonomous driving object detection semisupervised learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] U463.6[机械工程—车辆工程]