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

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 lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ 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.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives

“…As pointed out by a plenty of recent works (Wang et al 2016;Liu et al , 2019Liu et al , 2020Scheirer et al 2020;Yang et al 2020;Hahner et al 2019), the performance of high-level computer vision tasks, such as object detection and recognition, will deteriorate in the presence of various sensory and environmental degradation. In particular, Sakaridis et al (2018) studied the effect of image dehazing on semantic segmentation by a synthesized Foggy Cityscapes dataset with 20,550 images.…”

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark

“…UAV can also be difficult to detect due to poor visibility in adverse weather conditions, poor lighting, low-quality cameras (on other UAV) and buildings with a similar colour profile. Yang et al [53] survey methods to improve visibility for detection, including de-hazing, de-raining and lowlight enhancement. These methods improve detection results on well-researched objects such as faces, pedestrians and vehicles, but it is unclear whether they transfer to UAV.…”

Visibility Restoration: A Systematic Review and Meta-Analysis

“…The results demonstrated that the former methods favored human perception, whereas the latter methods favored numerical metrics. Recently, Yang et al [7] launched a challenge to evoke discussions and explorations regarding exploiting low-level image processing techniques in high-level vision tasks. The results were similar to those displayed by Pei et al [5], signifying large room for development.…”