Boosting Visual Recognition for Autonomous Driving in Real-world
  Degradations with Deep Channel Prior
Zhanwen Liu Yuhang Li Yang Wang Bolin Gao Yisheng An Xiangmo Zhao
The environmental perception of autonomous vehicles in normal conditions have
achieved considerable success in the past decade. However, various unfavourable
conditions such as fog, low-light, and motion blur will degrade image quality
and pose tremendous threats to the safety of autonomous driving. That is, when
applied to degraded images, state-of-the-art visual models often suffer
performance decline due to the feature content loss and artifact interference
caused by statistical and structural properties disruption of captured images.
To address this problem, this work proposes a novel Deep Channel Prior (DCP)
for degraded visual recognition. Specifically, we observe that, in the deep
representation space of pre-trained models, the channel correlations of
degraded features with the same degradation type have uniform distribution even
if they have different content and semantics, which can facilitate the mapping
relationship learning between degraded and clear representations in
high-sparsity feature space. Based on this, a novel plug-and-play Unsupervised
Feature Enhancement Module (UFEM) is proposed to achieve unsupervised feature
correction, where the multi-adversarial mechanism is introduced in the first
stage of UFEM to achieve the latent content restoration and artifact removal in
high-sparsity feature space. Then, the generated features are transferred to
the second stage for global correlation modulation under the guidance of DCP to
obtain high-quality and recognition-friendly features. Evaluations of three
tasks and eight benchmark datasets demonstrate that our proposed method can
comprehensively improve the performance of pre-trained models in real
degradation conditions. The source code is available at
https://github.com/liyuhang166/Deep_Channel_Prior