A small object detection method based on local maxima and SSD

Lei Bo; Tan Hai; Fan Qiang

doi:10.1117/12.2548076

18 December 2019 A small object detection method based on local maxima and SSD

Lei Bo, Tan Hai, Fan Qiang

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 113420Q (2019) https://doi.org/10.1117/12.2548076
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

Small object detection in complex scene is a difficult task in image processing. Normally, small objects in images are also weak objects where the contrast between targets and background is so subtle which makes it difficult to perceive. SSD(Single Shot MultiBox Detector) is one of the object detection method proved to be effective for normal size object detection, otherwise, unable to handle the small target task. A new small object detection method based on SSD is brought up in this paper. At first, a local maxima detector is performed in the image to obtain local maxima points in the image, which would be considered as the center of prior boxes for the subsequent object detection in feature maps of different levels. Secondly, the object extraction would be performed in con2_2 and conv3_3, that assures small objects does not be disappear in high level feature maps. Finally, a method to mark small object is brought up. The proposed method is performed in in several videos, which prove this method is feasible and effective.

Citation Download Citation

Lei Bo, Tan Hai, and Fan Qiang "A small object detection method based on local maxima and SSD", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420Q (18 December 2019); https://doi.org/10.1117/12.2548076

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