Transferring enhanced material knowledge via image quality enhancement and feature distillation for pavement condition identification - Zejiu Wu; Yuxing Zou; Boyang Liu; Zhijie Li; Donghong Ji; Hongbin Zhang (Scientific Reports)
Abstract In the context of rapid advancements in autonomous driving technology, ensuring passengers’ safety and comfort has become a priority. Obstacle or road detection systems, especially accurate pavement condition identification in unfavorable weather or time circumstances, play a crucial role in the safe operation and comfortable riding experience of autonomous vehicles. To this end, we propose a novel framework based on image quality enhancement and feature distillation (IQEFD) for detecting diverse pavement conditions during the day and night to achieve state classification. The IQEFD model first leverages ConvNeXt as its backbone to extract high-quality basic features. Then, a bidirectional fusion module embedded with a hybrid attention mechanism (HAM) is devised to effectively extract multi-scale refined features, thereby mitigating information loss during continuous upsampling and downsampling. Subsequently, the refined features are fused with the enhanced features extracted through the image enhancement network Zero-DCE to generate the fused attention features. Lastly, the enhanced features serve as the guidance online for the fused attention features through feature distillation, transferring enhanced material knowledge and achieving alignment between feature representations. Extensive experimental results on two publicly available datasets validate that IQEFD can accurately classify a variety of pavement conditions, including dry, wet, and snowy conditions, especially showing satisfactory and robust performance in noisy nighttime images. In detail, the IQEFD model achieves the accuracies of 98.04% and 98.68% on the YouTube-w-ALI and YouTube-w/o-ALI datasets, respectively, outperforming the state-of-the-art baselines. It is worth noting that IQEFD has a certain generalization ability on a classical material image dataset named MattrSet, with an average accuracy of 75.86%. This study provides a novel insight into pavement condition identification. The source code of IQEFD will be made available at https://github.com/rainzyx/IQEFD .