A-large-scale-container-dataset-and-a-baseline-method-for-container-hole-localization

A-large-scale-container-dataset-and-a-baseline-method-for-container-hole-localization, RTIP 2022 https://link.springer.com/article/10.1007/s11554-022-01199-y

Description

This is the first large-scale container dataset for automatic container handling in the station yard, containing 144 container videos, 1700 container images and 4810 container hole images, for benchmarking container hole location and detection. You can download the container dataset from BaiduYun(password:0cue) or GoogleDrive.

Citation

Please cite our papers if you find it useful:

Yunfeng Diao, Xin Tang, He Wang, Emma Taylor, Shirui Xiao, Mengtian Xie and Wenming Cheng. “A large-scale container dataset and a baseline method for container hole localization.” J. Real Time Image Process. 19 (2022): 577-589.

@article{Diao2022ALC, title={A large-scale container dataset and a baseline method for container hole localization}, author={Yunfeng Diao and Xin Tang and He-Nan Wang and Emma Taylor and Shirui Xiao and Mengtian Xie and Wenming Cheng}, journal={J. Real Time Image Process.}, year={2022}, volume={19}, pages={577-589} }

Contact

Please email Yunfeng Diao dyf@my.swjtu.edu.cn for further questions.