Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups
| Autor: | Christoph Stiller, Shuxiao Ding, Sascha Wirges |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Detector Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences Object (computer science) Grid 01 natural sciences Object detection Domain (software engineering) Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Pyramid (image processing) business 0105 earth and related environmental sciences |
| Zdroj: | IV |
| Popis: | We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios. Our goal is to train a robust object detector on grid maps generated from custom sensor data and setups. We first introduce a single-stage object detector for grid maps based on RetinaNet. We then extend our model by image- and instance-level domain classifiers at different feature pyramid levels which are trained in an adversarial manner. This allows us to train robust object detectors for unlabeled domains. We evaluate our approach quantitatively on the nuScenes and KITTI benchmarks and present qualitative domain adaptation results for unlabeled measurements recorded by our experimental vehicle. Our results demonstrate that object detection accuracy for unlabeled domains can be improved by applying our domain adaptation strategy. 6 pages, 5 figures, 4 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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