《表4 CCNet性能：CCNet:面向多光谱图像的高速船只检测级联卷积神经网络（英文）》

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《CCNet:面向多光谱图像的高速船只检测级联卷积神经网络（英文）》

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To demonstrate the efficiency of our CCNet，we compare it with the state-of-the-art deep-learning-based ship detection algorithm.The comparisons of the per-formances are listed in Table 6.Due to lack of evolution for small objects in previous works，we just compare the detection performance（Precision and recall）and detection speed.The precision and recall are evaluated under IOU 0.5 while Zhang et al[19].only provided under IOU0.4.The algorithms with multispectral image have higher performance than those algorithms with visible images.Our method owns the best precision compared with previous methods.Though the recall is less the Zhou et al.，the rate of scale variation of the largest ship and the smallest ship in our method is more than 20 times whereas only 4 times in the work by Zhou et al.[11].Thus，our CCNet is more robust than the method proposed by Zhou.As the ship occupy rate in our image dataset is far above the real remote sensing images，the FLOPS of our method is evaluated under the executive rate of REM and RDM is 5:1，which is also far above the real remote sensing images as the ship only occupies less 1%of the sea.Compared with the other methods，our method reduces the counts of FLOPS more than 5 times because the CCNet uses a cascaded model to extract ROIs and locate objects in ROIs.