[1]SHAN Yi,YANG Jinfu,WU Suishuo,et al.Skip feature pyramid network with a global receptive field for small object detection[J].CAAI Transactions on Intelligent Systems,2019,14(6):1144-1151.[doi:10.11992/tis.201905041]
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Skip feature pyramid network with a global receptive field for small object detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1144-1151 Column: 学术论文—机器学习 Public date: 2019-11-05
Title:
Skip feature pyramid network with a global receptive field for small object detection
Author(s):
SHAN Yi1; 2;  YANG Jinfu1; 2;  WU Suishuo1; 2;  XU Bingbing1; 2
1. Beijing University of Technology, Faculty of Information Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
Keywords:
skip feature pyramid network; global receptive field; object detection; deep learning; feature extraction; convolutional neural network; dilated convolution; image processing
CLC:
TP183
DOI:
10.11992/tis.201905041
Abstract:
With the development of deep learning, objects can be detected with high accuracy and efficiency. However, the detection of small objects remains challenging. The main reason for this is that the relationship between high-level semantic information and low-level feature maps is not fully utilized. To solve this problem, we propose a novel detection framework, called the skip feature pyramid network with a global receptive field, to improve the ability to detect small objects. Unlike previous detection architectures, the skip feature pyramid architecture fuses high-level semantic information with low-level feature maps to obtain detailed information. To extract global information from a network, we apply a global receptive field (GRF) with convolution kernels of different sizes and different dilated convolution steps. The experimental results on PASCAL VOC and MS COCO datasets show that the proposed approach realizes significant improvements over other comparable detection models.
References:
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