Small Object Detection with Multiple Receptive Fields

Abstract

Small object detection has been a problem in deep learning convolutional neural network models. A multi-rate dilated convolution module is proposed to form a feature map to locate small objects. Benefiting from the fact that the dilated convolution does not add extra complexity while maintaining the characteristics of the high-resolution feature map, this paper replaces the traditional convolution network by setting the dilated convolution with the ratio of 1, 2, and 5, and fuses the different convolution rate convolutions. The feature map branch, extracting high-level features, using the FPN algorithm as the basic network framework, combining a variety of receptive field feature maps, enhancing the detection ability of the algorithm for small objects, and the convergence accuracy is greatly improved. Experiments show that the proposed method achieves 81.9% MAP in the PASCAL VOC2007 dataset, which exceeds the traditional object detection algorithm.