Detection of Small Targets in Aerial Images Based on Feature Fusion

Abstract

AbstractMost existing target detection models are designed for image targets in natural scenarios and the detection results of small targets in aerial images are less than satisfactory. This paper tries to solve the locating and classification problem of small targets in aerial images from the perspectives of fusion between shallow features and deep features, receptive field enhancement, and a lightweight feature extraction network. To realize accurate detection of small targets in aerial images, on the basis of SSD (Single Shot MultiBox Detector), an aerial image target detection model MF-SSD based on multi-scale feature fusion and receptive field enhancement is proposed. In this model, a feature fusion sub-network is established, in which, the two-level fusion of shallow features and skip-connection fusion of intermediate features are provided. In addition, a multi-branch Inception structure D-Inception based on dilated convolution is utilized to achieve receptive field enhancement, which enables the system to better extract target features and adapt to the scale change of target. To address the increased computational complexity of the model after feature fusion and receptive field enhancement processing, a lightweight convolutional neural network based on Mobilenet is used to replace the basic network, which can not only reduce the complexity of the network model but also maintain high detection precision. The experimental results show that the target detection model MF-SSD can be used to effectively detect small targets in aerial images.KeywordsSmall target detectionFeature fusionReceptive field enhancementLightweight convolutional neural network (CNN)