@Article{jihpp.2021.010065,
AUTHOR = {Lei Zhao, Ming Zhao},
TITLE = {Feature-Enhanced RefineDet: Fast Detection of Small Objects},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {1--8},
URL = {http://www.techscience.com/jihpp/v3n1/42325},
ISSN = {2637-4226},
ABSTRACT = {Object detection has been studied for many years. The convolutional 
neural network has made great progress in the accuracy and speed of object 
detection. However, due to the low resolution of small objects and the 
representation of fuzzy features, one of the challenges now is how to effectively 
detect small objects in images. Existing target detectors for small objects: one is 
to use high-resolution images as input, the other is to increase the depth of the 
CNN network, but these two methods will undoubtedly increase the cost of 
calculation and time-consuming. In this paper, based on the RefineDet network 
framework, we propose our network structure RF2Det by introducing Receptive 
Field Block to solve the problem of small object detection, so as to achieve the 
balance of speed and accuracy. At the same time, we propose a Medium-level 
Feature Pyramid Networks, which combines appropriate high-level context 
features with low-level features, so that the network can use the features of both 
the low-level and the high-level for multi-scale target detection, and the accuracy 
of the small target detection task based on the low-level features is improved. 
Extensive experiments on the MS COCO dataset demonstrate that compared to 
other most advanced methods, our proposed method shows significant 
performance improvement in the detection of small objects.},
DOI = {10.32604/jihpp.2021.010065}
}