Display options
Cite
Yang W, Yuan Y, Ren W, et al. Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study. IEEE Trans Image Process. 2020;doi: 10.1109/TIP.2020.2981922.
Yang, W., Yuan, Y., Ren, W., Liu, J., Scheirer, W. J., Wang, Z., Zhang, T., Zhong, Q., Xie, D., Pu, S., Zheng, Y., Qu, Y., Xie, Y., Chen, L., Li, Z., Hong, C., Jiang, H., Yang, S., Liu, Y., Qu, X., Wan, P., Zheng, S., Zhong, M., Su, T., He, L., Guo, Y., Zhao, Y., Zhu, Z., Liang, J., Wang, J., Chen, T., Quan, Y., Xu, Y., Liu, B., Liu, X., Sun, Q., Lin, T., Li, X., Lu, F., Gu, L., Zhou, S., Cao, C., Zhang, S., Chi, C., Zhuang, C., Lei, Z., Li, S. Z., Wang, S., Liu, R., Yi, D., Zuo, Z., Chi, J., Wang, H., Wang, K., Liu, Y., Gao, X., Chen, Z., Guo, C., Li, Y., Zhong, H., Huang, J., Guo, H., Yang, J., Liao, W., Yang, J., Zhou, L., Feng, M., & Qin, L. (2020). Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, . https://doi.org/10.1109/TIP.2020.2981922
Yang, Wenhan, et al. "Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study." IEEE transactions on image processing : a publication of the IEEE Signal Processing Society vol. (2020). doi: https://doi.org/10.1109/TIP.2020.2981922
Yang W, Yuan Y, Ren W, Liu J, Scheirer WJ, Wang Z, Zhang T, Zhong Q, Xie D, Pu S, Zheng Y, Qu Y, Xie Y, Chen L, Li Z, Hong C, Jiang H, Yang S, Liu Y, Qu X, Wan P, Zheng S, Zhong M, Su T, He L, Guo Y, Zhao Y, Zhu Z, Liang J, Wang J, Chen T, Quan Y, Xu Y, Liu B, Liu X, Sun Q, Lin T, Li X, Lu F, Gu L, Zhou S, Cao C, Zhang S, Chi C, Zhuang C, Lei Z, Li SZ, Wang S, Liu R, Yi D, Zuo Z, Chi J, Wang H, Wang K, Liu Y, Gao X, Chen Z, Guo C, Li Y, Zhong H, Huang J, Guo H, Yang J, Liao W, Yang J, Zhou L, Feng M, Qin L. Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study. IEEE Trans Image Process. 2020 Mar 27; doi: 10.1109/TIP.2020.2981922. Epub 2020 Mar 27. PMID: 32224457.
Copy Download .nbib Format: NLM
Share it on

IEEE Trans Image Process. 2020 Mar 27; doi: 10.1109/TIP.2020.2981922. Epub 2020 Mar 27.

Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

Wenhan Yang, Ye Yuan, Wenqi Ren, Jiaying Liu, Walter J Scheirer, Zhangyang Wang, Taiheng Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, Yuqiang Zheng, Yanyun Qu, Yuhong Xie, Liang Chen, Zhonghao Li, Chen Hong, Hao Jiang, Siyuan Yang, Yan Liu, Xiaochao Qu, Pengfei Wan, Shuai Zheng, Minhui Zhong, Taiyi Su, Lingzhi He, Yandong Guo, Yao Zhao, Zhenfeng Zhu, Jinxiu Liang, Jingwen Wang, Tianyi Chen, Yuhui Quan, Yong Xu, Bo Liu, Xin Liu, Qi Sun, Tingyu Lin, Xiaochuan Li, Feng Lu, Lin Gu, Shengdi Zhou, Cong Cao, Shifeng Zhang, Cheng Chi, Chubin Zhuang, Zhen Lei, Stan Z Li, Shizheng Wang, Ruizhe Liu, Dong Yi, Zheming Zuo, Jianning Chi, Huan Wang, Kai Wang, Yixiu Liu, Xingyu Gao, Zhenyu Chen, Chang Guo, Yongzhou Li, Huicai Zhong, Jing Huang, Heng Guo, Jianfei Yang, Wenjuan Liao, Jiangang Yang, Liguo Zhou, Mingyue Feng, Likun Qin

PMID: 32224457 DOI: 10.1109/TIP.2020.2981922

Abstract

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.

Publication Types