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CODA:用于自动驾驶目标检测的真实道路拐角案例数据集

CODA:用于自动驾驶目标检测的真实道路拐角案例数据集

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Autonomous Driving 2D Box

CODA is the world's first real-world self-driving corner case dataset of 1500 scenes (frames) containing nearly 6K c......

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    README.md

    CODA is the world's first real-world self-driving corner case dataset of 1500 scenes (frames) containing nearly 6K corner cases.   

    CODA is split into a validation set of 1000 images and a test set of 500 images.                   

    The validation set contains 4008 objects of 27 object categories, whereas the test set contains 1929 objects of 34 object categories, including 7 categories absent in the test set.

    Validation set

    Corner case annotations are stored in "val/corner_case.json" in COCO-compatible format.                   

    Out of the 1000 scenes of the validation set, 717 are taken from ONCE, 89 are taken from nuScenes, and 194 are taken from KITTI.                   

    Due to license issues, for nuScenes and KITTI, only corner case annotations and the correponding sample indices/tokens of original datasets are provided ("val/kitti_indices.json" and "val/nuscenes_sample_tokens.json").                  

     For ONCE, in addition to corner case annotations, we also provide the front-view images captured by the camera named "cam03".                   

    The images taken from onCE are named in the format of "[sequence_id]_[frame_id].jpg" (000001_1616005007200.jpg, for example).                  

     The two identifiers ("sequence_id" and "frame_id") can be used to extract other data (e.g., lidar point clouds) from the onCE dataset if needed.                    

    Data Format

    The annotation file keeps consistent with the COCO format and contains three keys: "images", "categories" and "annotations".

    "images": {
            "file_name":         -- File name.
            "id":                -- Unique image id.
            "height":          -- Height of the image.
            "width":           -- Width of the image.
            "period":            -- Period tag.
            "weather":           -- Weather tag.
    }

                       

    "annotations": {
            "image_id":          -- The image id for this annotation.
            "category_id":       -- The category id.
            "bbox":             -- Coordinate of boundingbox [x, y, w, h].
            "area":            -- Area of this annotation (w * h).
            "id":                -- Unique annotation id.
            "iscrowd":           -- Whether this annotation is crowd.
    }

                       

    "categories": {
            "name":              -- Unique category name.
            "id":                -- Unique category id.
            "supercategory":     -- The supercategory for this category.
    }

                                           

    Data Annotation

    Image domain tags (i.e., periods and weather conditions) and 2D bounding boxes with classes for all CODA images.

    Semantic Labels

    CODA annotation can be grouped into 7 super-categories including pedestrian, cyclist, vehicle, animal, traffic facility, obstruction and misc, which can be further divided into 34 fine-grained categories. Moreover, these categories can also be divided into two collections, namely 1) instances of novel classes and 2) novel instances of common classes. As the names suggest, common classes stand for common object categories annotated by existing autonomous driving benchmarks, such as cars and pedestrians, whereas novel classes stand for the opposites, such as dogs and strollers.

    Domain Tags

    CODA also provides domain tags for all images including the periods and weather conditions. Specifically, we annotate the period tags to be either day or night and select the weather condition tags from sunny, cloudy and rainy. We hope the image domain tags can help researchers dig into the underlying reasons of corner cases for reliable object detection.


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