Frequency-based dual attention network for nighttime semantic segmentation

Nighttime images suffer from exposure imbalance, leading to significant challenges in nighttime semantic segmentation. Given this, we propose a frequency-based dual attention network (FDANet) for nighttime semantic segmentation. To address the problem of targets hidden in the background caused by uneven exposure and low contrast that are difficult to distinguish, we propose a multi-spectral dual attention module to deeply explore hidden features from frequency information. In addition, to further utilize the relationship between frequency domain information and spatial domain features and guide the extraction of contextual features, spatial-frequency aggregation module is proposed to align and fuse features from different domains. Moreover, self-attention dilated convolution pyramid module is proposed to expand the effective receptive field and enhance the ability to identify multi-scale targets under uneven exposure. Extensive experiments on NightCity, ACDC, and BDD show that FDANet achieves an impressive balance between accuracy and speed. Specifically, FDANet achieves 53.76% MIoU, 49.47% MIoU, and 39.70% MIoU on NightCity, ACDC-Night, and BDD-Night, respectively. Notably, it attains these results with remarkable processing speeds of 111 FPS, 66 FPS, and 104 FPS for input resolutions of 512×1024, 1080×1920, and 720×1280, respectively.