Object detection using improved YOLOv3-tiny based on pyramid pooling

Abstract

Research on object detection algorithms with higher accuracy and faster detection speed is currently the main concern. In order to improve detection performance, an improved object detection algorithm using YOLOv3-tiny based on pyramid pooling is proposed. First, an improved pyramid pooling module using adaptive average pooling is designed to efficiently extract global feature information, and then combine the module with YOLOv3-tiny to explore the impact of different combinations on the detection results. The experiment used PASCAL VOC2007 trainval and all PASCAL VOC2012 for training and validation, and used PASCAL VOC2007 test for testing. Experimental results show that the proposed network improves mAP by 1.8% compared to YOLOv3-tiny while the detection speed is almost the same, which better achieves the balance of detection speed and accuracy.