计算机科学
能见度
实时计算
水准点(测量)
人工智能
除雪
组分(热力学)
雾
恶劣天气
薄雾
资源(消歧)
特征(语言学)
雪
计算机网络
哲学
地理
气象学
物理
光学
热力学
语言学
大地测量学
作者
Aryan Mehra,Murari Mandal,Pratik Narang,Vinay Chamola
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-07-01
卷期号:22 (7): 4256-4266
被引量:57
标识
DOI:10.1109/tits.2020.3013099
摘要
Adverse weather conditions such as fog, haze, snow, mist and glare create visibility problems for applications of autonomous vehicles. To ensure safe and smooth operations in frequent bad weather scenarios, image dehazing is crucial to any vehicular motion and navigation task on road or air. Moreover, the commonly deployed mobile systems are resource constrained in nature. Therefore, it is important to ensure memory, compute and run-time efficiency of dehazing algorithms. In this manuscript we propose ReViewNet, a fast, lightweight and robust dehazing system suitable for autonomous vehicles. The network uses components like spatial feature pooling, quadruple color-cue, multi-look architecture and multi-weighted loss to effectively dehaze images captured by cameras of autonomous vehicles. The effectiveness of the proposed model is analyzed by exhaustive quantitative evaluation on five benchmark datasets demonstrating its supremacy over other existing state-of-the-art methods. Further, a component-wise ablation and loss weight ratio analysis demonstrates the contribution of each and every component of the network. We also show the qualitative analysis with special use cases and visual responses on distinctive vehicular vision instances, establishing the effectiveness of the proposed method in numerous hazy weather conditions for autonomous vehicular applications.
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