Domain Adaptive YOLO for One-Stage Cross-Domain Detection

• URL: http://arxiv.org/abs/2106.13939v1
• Date: Sat, 26 Jun 2021 04:17:42 GMT
• Title: Domain Adaptive YOLO for One-Stage Cross-Domain Detection
• Authors: Shizhao Zhang, Hongya Tuo, Jian Hu, Zhongliang Jing
• Abstract summary: Domain Adaptive YOLO (DA-YOLO) is proposed to improve cross-domain performance for one-stage detectors. We evaluate our proposed method on popular datasets like Cityscapes, KITTI, SIM10K and etc.
• Score: 4.596221278839825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain shift is a major challenge for object detectors to generalize well to real world applications. Emerging techniques of domain adaptation for two-stage detectors help to tackle this problem. However, two-stage detectors are not the first choice for industrial applications due to its long time consumption. In this paper, a novel Domain Adaptive YOLO (DA-YOLO) is proposed to improve cross-domain performance for one-stage detectors. Image level features alignment is used to strictly match for local features like texture, and loosely match for global features like illumination. Multi-scale instance level features alignment is presented to reduce instance domain shift effectively , such as variations in object appearance and viewpoint. A consensus regularization to these domain classifiers is employed to help the network generate domain-invariant detections. We evaluate our proposed method on popular datasets like Cityscapes, KITTI, SIM10K and etc.. The results demonstrate significant improvement when tested under different cross-domain scenarios.

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