计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 157-164.doi: 10.11896/jsjkx.210600240
王灿1,2, 刘永坚1, 解庆1,2, 马艳春1
WANG Can1,2, LIU Yong-jian1, XIE Qing1,2, MA Yan-chun1
摘要: 与Anchor Based目标检测算法类似,基于特征点的Anchor Free目标检测算法也面临着在正负样本划分中存在模糊样本的问题,即根据特定阈值和特征点位置划分非正即负的训练样本,网络在对特征点位置处在临界值附近的样本进行训练时会产生较大的损失,使得网络将注意力过于集中在这些模糊样本上,降低了网络的整体检测性能。针对此情况,提出从软标签、损失函数和权重优化3个方面对基于特征点的Anchor Free目标检测算法进行改进,通过充分利用Center Ness参数来缓解模糊样本对网络性能的影响,提高目标检测的准确率。为证明所提方法的有效性,分别在经典的Pascal VOC数据集和MS COCO数据集上使用FCOS目标检测器进行对比实验,最终将检测器在Pascal VOC数据集上的mAP提升至82.16%(提升约1.31%),在MS COCO数据集上的AP50-95提升至35.8%(提升约1.3%)。
中图分类号:
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