Airport runway foreign object detection adapted to low-light environments

Bowen Chen; Weixing Chen; Zhang Wen

doi:10.1117/12.3025709

1 April 2024 Airport runway foreign object detection adapted to low-light environments

Bowen Chen, Weixing Chen, Zhang Wen

Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130810P (2024) https://doi.org/10.1117/12.3025709
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China

Abstract

To address the low accuracy of existing nighttime airport runway foreign object detection algorithms, this paper proposes the Low-Light YOLOv5s FOD algorithm by integrating low-light enhancement and YOLOv5 detection concepts. During the training of the low-light enhancement network, the brightness layer channel, normalized for brightness, is incorporated into the attention mechanism, allowing the model to focus more on dark areas. Subsequently, dark-light FOD images undergo low-light enhancement. On the YOLOv5s detection network, multiscale feature fusion, a small object detection layer, and the NWD loss function are employed to enhance small object detection and address position sensitivity issues. ODConv, a full-dimensional dynamic convolution, replaces the standard convolution, further improving accuracy with multidimensional complementary attention mechanisms. Finally, foreign object detection is performed on enhanced images. Experimental results indicate a 12.6% improvement in NIQE scores for the improved EnlightenGAN restored images and a 4.3% increase in mAP for the improved YOLOv5s compared to the original model. In nighttime environments, the proposed algorithm achieves an average detection accuracy of 99.39%, a 67.99% improvement over the original algorithm without low-light enhancement, at a detection speed of 50.30 frames/s. Balancing accuracy and real-time performance, this algorithm effectively addresses false positives and misses in FOD detection during nighttime conditions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bowen Chen, Weixing Chen, and Zhang Wen "Airport runway foreign object detection adapted to low-light environments", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130810P (1 April 2024); https://doi.org/10.1117/12.3025709

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

Object detection

Detection and tracking algorithms

Target detection

Small targets

Environmental sensing

Image processing

Image enhancement

Keywords/Phrases

Search In:

Publication Years