{"url":"/dataset/cctsdb-aug","name":"CCTSDB-AUG","full_name":"From CCSPNet-Joint，IJCNN 2024","description_markdown":"The CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) is an existing dataset for traffic sign detection. It consists of nearly 20,000 images of Chinese road traffic scenes, including around 40,000 annotated images of traffic signs. While most scenes in the dataset are captured under natural weather conditions, challenges include foggy, rainy, and blurry perspectives. To facilitate our research, we created a dataset called CCTSDB-AUG based on CCTSDB. This augmented dataset includes images with foggy, rainy, and blurry perspectives. We applied random haze, raindrop, and motion blur effects to generate these augmented images, simulating real-world extreme conditions. Image augmentation was performed using the Albumentations library in Python, allowing us to construct images with various weather effects. The CCTSDB-AUG dataset contains images with different extreme conditions,. These extreme conditions are proportionally represented throughout the dataset.","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/haoqinhong/cctsdb-aug-ijcnn-2024/data","introduced_date":"2023-09-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/ccspnet-joint-efficient-joint-training-method","title":"CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions","first_author":"Haoqin Hong","url":null},"license":null,"modalities":[],"tasks":[{"name":"Traffic Sign Detection","url":"/task/traffic-sign-detection","datasets_with_task":"/datasets/task/traffic-sign-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CCTSDB-AUG"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/traffic-sign-detection-on-cctsdb-aug","task":"Traffic Sign Detection","dataset_variant":"CCTSDB-AUG","rows":2,"metrics":["Averaged Precision","avg-mAP (0.1-0.5)"],"first_row_in_archive_order":{"model":"CCSPNet-Joint","paper":"/paper/ccspnet-joint-efficient-joint-training-method","metrics":{"Averaged Precision":"0.951","avg-mAP (0.1-0.5)":"0.914"},"code_links":[{"title":"haoqinhong/ccspnet-joint","url":"https://github.com/haoqinhong/ccspnet-joint"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ccspnet-joint-efficient-joint-training-method","title":"CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions","date":"2023-09-13","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}