Spatial Attention Based Real-Time Object Detection Network for Internet of Things Devices

Abstract

Target detection algorithms for Internet of things (IoT) devices often require both high real-time performance and low computational complexity. Real-time object detection network: You Only Look Once Version 3 (YOLOv3) makes full use of multi-scale features to detect objects by using feature pyramid network structure, and achieves good performance on the premise of guaranteeing fast detection speed. The feature pyramid network of YOLOv3 includes bottom-up feature extraction, top-down sampling and lateral connection of low-level detail features and high-level semantic features. But not all features are useful for object detection. In this article, a novel object detection network Spatial Attention based YOLOv3 (SA-YOLOv3) is proposed. The proposed method adds spatial attention network to the top-down sampling process. The spatial attention network calculates the feature weight matrix based on the up-sampling feature map. SA-YOLOv3 uses the feature weight matrix to filter low-level features and retain more valuable features. Finally, the selected low-level feature map and high-level feature map are concatenated together and feature maps with both spatial information and rich semantic information are obtained. The experimental results on PASCAL VOC2012 datasets and RSOD datasets show that SA-YOLOv3 outperforms YOLOv3.