基于Trans-nightSeg的夜间道路场景语义分割方法
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李灿林,张文娇,邵志文,马利庄,王新玥
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Semantic segmentation method on nighttime road scene based on Trans-nightSeg
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Canlin LI,Wenjiao ZHANG,Zhiwen SHAO,Lizhuang MA,Xinyue WANG
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表 2 不同方法在Dark Zurich-test上的IoU结果 |
Tab.2 Results of IoU for different methods on Dark Zurich-test |
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类别 | IOU/% | DMAda | GCMA | MGCDA | DANNet | DANIA | 本文方法 | 道路 | 75.5 | 81.7 | 80.3 | 88.6 | 90.8 | 93.1 | 人行道 | 29.1 | 46.9 | 49.3 | 53.4 | 59.7 | 69.4 | 建筑 | 48.6 | 58.8 | 66.2 | 69.8 | 73.7 | 82.2 | 墙体 | 21.3 | 22 | 7.8 | 34 | 39.9 | 52.1 | 栅栏 | 14.3 | 20 | 11 | 20 | 26.3 | 27.1 | 杆 | 34.3 | 41.2 | 41.4 | 25 | 36.7 | 57.0 | 交通灯 | 36.8 | 40.5 | 38.9 | 31.5 | 33.8 | 50.8 | 交通标志 | 29.9 | 41.6 | 39 | 35.9 | 32.4 | 56.2 | 植物 | 49.4 | 64.8 | 64.1 | 69.5 | 70.5 | 77.8 | 地形 | 13.8 | 31 | 18 | 32.2 | 32.1 | 32.3 | 天空 | 0.4 | 32.1 | 55.8 | 82.3 | 85.1 | 89.3 | 行人 | 43.3 | 53.5 | 52.1 | 44.2 | 43.0 | 55.0 | 骑手 | 50.2 | 47.5 | 53.5 | 43.7 | 42.2 | 50.4 | 汽车 | 69.4 | 72.5 | 74.7 | 54.1 | 72.8 | 81.9 | 卡车 | 18.4 | 39.2 | 66 | 22 | 13.4 | 0.0 | 公共汽车 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 17.9 | 火车 | 27.6 | 49.6 | 37.5 | 40.9 | 71.6 | 88.5 | 摩托车 | 34.9 | 30.7 | 29.1 | 36 | 48.9 | 46.4 | 自行车 | 11.9 | 21 | 22.7 | 24.1 | 23.9 | 36.3 | mIoU/% | 32.1 | 42 | 42.5 | 42.5 | 47.2 | 56.0 |
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