计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 205-218.doi: 10.11896/jsjkx.220500260
杜紫薇1, 周恒1,2, 李承阳1,3, 李忠博1, 谢永强1, 董昱辰1, 齐锦1
DU Zi-wei1, ZHOU Heng1,2, LI Cheng-yang1,3, LI Zhong-bo1, XIE Yong-qiang1, DONG Yu-chen1, QI Jin1
摘要： 小目标检测是计算机视觉领域最具挑战性的问题之一。相比大目标,小目标覆盖面积小,空间分辨率低,可用特征少,检测效果通常不理想。近年来,基于深度卷积神经网络的小目标检测算法蓬勃发展,并在卫星遥感、无人驾驶等领域取得了重大成就。文中对国内外现有的小目标检测算法进行了归类、分析和比较。首先介绍小目标检测的难点和常用的数据集;接着分别从骨干网络、金字塔结构、锚框设计、优化目标、增益组件5个方面系统地梳理了已有检测算法,为进一步改进小目标检测算法的性能提供了思路;然后对现有小目标检测算法进行全面总结,并比较分析了列举算法在常用数据集上的性能;最后介绍了小目标检测的应用前景,并对该领域未来的研究方向做出了展望。
中图分类号:
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