Image classification and object detection for low-resolution images and small objects with SPD-Conv
Image classification and object detection for low-resolution images and small objects with SPD-Conv
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects
arXiv paper abstract https://arxiv.org/abs/2208.03641v1
arXiv PDF paper https://arxiv.org/pdf/2208.03641v1.pdf
Convolutional neural networks (CNNs) ... in ...image classification and object detection ... degrades rapidly on tougher tasks where images are of low resolution or objects are small.
... this roots in ... the use of strided convolution and/or pooling layers, which results in a loss of fine-grained information and learning of less effective feature representations.
... propose a new CNN building block called SPD-Conv in place of each strided convolution layer and each pooling layer (thus eliminates them altogether).
SPD-Conv is comprised of a space-to-depth (SPD) layer followed by a non-strided convolution (Conv) layer, and can be applied in most if not all CNN architectures.
... create new CNN architectures by applying SPD-Conv to YOLOv5 and ResNet
... approach significantly outperforms state-of-the-art deep learning models ... on ... low-resolution images and small objects ...
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