Open Access

ARTICLE

Feature-Enhanced RefineDet: Fast Detection of Small Objects

Lei Zhao^*, Ming Zhao

School of Computer Science and Engineering, Central South University, Changsha, China

* Corresponding Author: Lei Zhao. Email:

Journal of Information Hiding and Privacy Protection 2021, 3(1), 1-8. https://doi.org/10.32604/jihpp.2021.010065

Received 30 August 2020; Accepted 14 January 2021; Issue published 21 April 2021

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.

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Keywords

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