Research on Bidirectional Attenuation Loss Method for Rotating Object Detection in Remote Sensing Image
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摘要: 遥感图像中的目标检测技术是计算机视觉领域的热点研究之一。为了适应遥感图像中的复杂背景和任意方向的目标，主流的目标检测模型均采用旋转检测方法。然而，用于旋转检测的定位损失函数通常存在变化趋势与实际偏斜交并比(Intersection-over-Union, IoU)的变化趋势不一致的问题。为此，该文提出一种新的面向旋转目标检测的双向衰减损失方法。具体而言，该方法通过高斯乘积模拟偏斜IoU，并依据预测位置的偏差从两个方向衰减乘积。双向衰减损失能够反映由位置偏差引起的偏斜IoU变化，其变化趋势与偏斜IoU有着更强的一致性，并且与其他相关方法相比性能更好。在DOTAv1.0数据集上的实验表明，所提方法在多种基底函数和不同精度条件下都是有效的。Abstract: Object detection in remote sensing image is one of the hot research topics in the field of remote sensing. In order to adapt to complex backgrounds and multi-directional objects in remote sensing images, the mainstream object detection model uses rotation detection method. However, most of positioning losses used for rotation detection generally has the problem that its trend is inconsistent with the trend of SkewIoU(Skew Intersection-over-Union). To solve this problem, a new bidirectional attenuation loss for rotating object detection is designed. Specifically, this method simulates SkewIoU by Gaussian product, and attenuates the product from two directions according to the deviation of the predicted position. The bidirectional attenuation loss has stronger trend-level alignment with SkewIoU and works better compared with other methods, thanks to its ability to reflect the SkewIoU change caused by position deviation. Experiments on DOTAv1.0 show the effectiveness of this method of various loss forms and different accuracy conditions.
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Key words:
- Remote sensing image /
- Object detection /
- Deep neural network /
- Loss function
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表 1 不同$\lambda $取值对RetinaNet网络检测性能的影响
$\lambda $ mAP(%) 1.60 70.59 1.70 70.71 1.75 71.24 1.80 71.22 1.90 70.12 
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表 2 基于不同损失计算方式的目标检测精度比较
自变量x $ - \ln \left( {x + \varepsilon } \right)$ $1 - x$ ${{\rm{e}}^{1 - x} } - 1$ KFIoU 69.35 70.07 70.30 BAIoU 71.01(+1.66) 70.93(+0.86) 71.24(+0.94)
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表 3 基于不同损失函数的目标检测精度比较
损失函数 AP50 AP75 AP50:95 KFIoU 70.30 33.16 34.45 BAIoU 71.24(+0.94) 36.58(+3.42) 37.06(+2.61)
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表 5 不同损失函数在DOTAv1.0中5类典型目标的检测结果对比(%)
网络模型 损失函数 飞机 小型汽车 大型汽车 船 直升机 mAP RetinaNet SmoothL1[7] 70.90 56.30 48.40 64.80 40.31 56.14 GWD[18] 79.98 68.55 66.53 74.18 52.75 68.39 KFIoU 83.56 74.46 67.69 76.43 59.36 70.30 BAIoU 85.13 74.01 70.29 76.02 60.77 71.24 R3Det DCL[16] 85.14 71.02 79.56 85.62 54.45 75.15 KLD[19] 87.41 73.43 83.07 87.00 60.73 78.32 KFIoU 87.61 75.65 84.06 88.38 62.01 79.54 BAIoU 87.70 76.45 85.02 89.22 62.34 80.14
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