A Method of Small Object Detection Based on Improved Deep Learning
Changgeng Yu, Kai Liu, Wei Zou · Optical Memory and Neural Networks · 2020
Abstract In this paper, a parallel SSD (Single Shot MultiBox Detector) fusion network based on inverted residual structure (IR-PSN) is proposed to solve the problems of the lack of extracted feature information and the unsatisfactory effect of small object detection by deep learning. Firstly, the Inverted Residual Structure (IR) is adopted into the SSD network to replace the pooling layer. The improved SSD network is called deep network of IR-PSN to extract high-level feature information of the image. Secondly, a shallow network based on the inverted residual structure is constructed to extract low-level feature information of the image. Finally, the shallow network is fused with the deep network to avoid the lack of small object feature information and improve the detection rate of small object. The experimental results show that the proposed method has satisfied results for small object detection under the premise of ensuring the accuracy rate P and recall rate R of the comprehensive object detection.