Tiny-RetinaNet: a one-stage detector for real-time object detection

Miao Cheng; Jianan Bai; Luyi Li; Qing Chen; Xiangming Zhou; Hequn Zhang; Peng Zhang

doi:10.1117/12.2557264

3 January 2020 Tiny-RetinaNet: a one-stage detector for real-time object detection

Miao Cheng, Jianan Bai, Luyi Li, Qing Chen, Xiangming Zhou, Hequn Zhang, Peng Zhang

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730R (2020) https://doi.org/10.1117/12.2557264
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In this paper, we present a new one stage detector for object detection. In order to meet the requirements of real-time detection, we use MobileNetV2-FPN as backbone for feature extraction. The lightweight depthwise separable convolutions can improve the speed of detection and make the model smaller. In order to improve the accuracy of our small detection model, we add Stem Block into backbone and we add SEnet in front of two task-specific subnets. The stem block can reduce the information loss from raw input images. The SEnet can enhance useful features from backbone network and suppress features that are little use to two-specific tasks. Inspired by RetinaNet, we also use Focal Loss as our classification loss function. We measure our performance on PASCAL VOC2007 and PASCAL VOC2012. Our detector with 300300 input achieves 73.8% mAP on VOC2007 test, 71.4% mAP on VOC2012 test. And our detector can run at 97FPS and the number of parameters is only 7.7M that meets the requirements of real-time detection. The accuracy of our detector is close to SSD, our detector uses about only 1/3 parameters to SSD. Keywords: One-

Citation Download Citation

Miao Cheng, Jianan Bai, Luyi Li, Qing Chen, Xiangming Zhou, Hequn Zhang, and Peng Zhang "Tiny-RetinaNet: a one-stage detector for real-time object detection", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730R (3 January 2020); https://doi.org/10.1117/12.2557264

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Sensors

Convolution

Convolutional neural networks

Bismuth

Feature extraction

Detection and tracking algorithms

Evolutionary algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years