Intelligent monitoring small target detection algorithm based on improved faster R-CNN

Hongbo Yu; Duojia Zhu; Qipeng Zhang; Weiding Wang

doi:10.1117/12.2680509

31 May 2023 Intelligent monitoring small target detection algorithm based on improved faster R-CNN

Hongbo Yu, Duojia Zhu, Qipeng Zhang, Weiding Wang

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270439 (2023) https://doi.org/10.1117/12.2680509
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

Current applications of the small target detection algorithm include enterprise logo detection, small face detection, pedestrian detection, traffic sign detection, automatic driving, remote sensing image detection, and criminal investigation. Practical applications are primarily still in the research phase. Consequently, this paper proposes an optimized small target detection algorithm on the basis of Faster R-CNN. Utilizing ResNet-18 with fewer convolutional layers in place of ResNet-50 as the backbone network of the original Faster R-CNN algorithm, the feature extraction network is strengthened, reducing the number of parameters of the algorithm. Moreover, by analyzing the characteristics of small targets, we can eliminate missed detection and false detection of small targets, Faster R-CNN algorithm uses feature fusion to amplify the contrast between small target features and background features in the feature pyramid network; Enhance prediction box deviations caused by pooling regions of interest by utilizing ROI Align. Lastly, the improved algorithm is experimentally verified on the MSCOCO data set. The faster R-CNN small target detection algorithm proposed in this thesis improves the optimized algorithm’s average accuracy from 89.14% to 93.39%, which is 4.25% higher than the original Faster R-CNN algorithm. Intelligent monitoring significantly enhances the detection and recognition of smaller and medium-sized targets.

Citation Download Citation

Hongbo Yu, Duojia Zhu, Qipeng Zhang, and Weiding Wang "Intelligent monitoring small target detection algorithm based on improved faster R-CNN", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270439 (31 May 2023); https://doi.org/10.1117/12.2680509

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
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Target detection

Small targets

Feature extraction

Education and training

Target recognition

Interpolation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years