计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 198-206.doi: 10.11896/jsjkx.210800214
潘毅, 王丽萍
PAN Yi, WANG Li-ping
摘要: 当前,以卷积神经网络为基础的目标检测算法大多存在缺少对有价值的上下文信息的合理利用以及易对困难目标漏检等问题。针对这些问题,提出了一种基于改进拆分注意力网络的目标检测算法。首先,引入拆分注意力机制,将多通道结构与注意力机制相结合,提升其特征表示。然后,在网络的卷积层中使用多尺度卷积取代传统的卷积操作,增强了神经网络对尺度变化的敏感性。最后,将改进的网络应用于Faster R-CNN中,并在Pascal VOC数据集和MS COCO数据集上进行实验。所提算法在不增加超参数量及计算复杂度的情况下,其mAP相较于原始算法分别提升了1.6%和2.4%,且对比其他算法也有所优势,验证了所提算法的良好性能。
中图分类号:
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