Object detection using improved YOLOv3-tiny based on pyramid pooling

Ruiqiang Liang; Tiejun Yang

doi:10.1117/12.2599401

30 June 2021 Object detection using improved YOLOv3-tiny based on pyramid pooling

Ruiqiang Liang, Tiejun Yang

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780E (2021) https://doi.org/10.1117/12.2599401
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

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.

Citation Download Citation

Ruiqiang Liang and Tiejun Yang "Object detection using improved YOLOv3-tiny based on pyramid pooling", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780E (30 June 2021); https://doi.org/10.1117/12.2599401

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