A multi-scale feature fusion target detection algorithm

Abstract

For existing Faster R-CNN and single shot multibox detector (SSD) target detection algorithms, they all have the problem of low object detection accuracy under small target conditions. This paper proposes a general and effective target detection algorithm and the detection accuracy has greatly improved for smaller targets. The algorithm is divided into two parts. In the first part, in the feature extraction process, the feature map extracted by the basic feature extraction network is deconvoluted and merged with the previous layer feature map to generate Multi-scale feature maps with rich semantics and high resolution. Using proposed multi-scale feature maps to generate proposals. The second part uses the generated proposals to be sent to the Faster R-CNN network for classification and detection. Experiments show that using this algorithm for target detection can not only improve the recall of proposals, but also improve the accuracy of target detection, especially for small targets. The algorithm provides a new idea for small target detection.