Light source influence Matrix-YOLO: an object detection framework for low-visibility conditions

Xinwei Yao; Zitao Tu; Xinge Zhang; Yuchen Zhang

doi:10.1117/12.3029425

22 May 2024 Light source influence Matrix-YOLO: an object detection framework for low-visibility conditions

Xinwei Yao, Zitao Tu, Xinge Zhang, Yuchen Zhang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762O (2024) https://doi.org/10.1117/12.3029425
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Contemporary object detection algorithms have achieved significant advancements in various fields. However, existing object detection algorithms suffer from reduced image clarity and increased noise under low visibility conditions, resulting in a decline in algorithm accuracy. To address this issue, we propose a novel Light Source Influence Matrix-YOLO (LSIM-YOLO) framework. we employ the LSIM algorithm to enhance the contrast of objects within the images. Conventional methods for object detection under low visibility conditions tend to overlook latent information within the images. To mitigate this, we introduce the se-ECA (Spatial Excitation and Channel Attention) mechanism to augment spatial information in features, which can enhance feature quality, thereby improving the performance of visual tasks under low visibility. Furthermore, we address the limitations of existing object detection networks in extracting features from low visibility images by replacing traditional strided convolution layers in the CNN architecture with Spatial-to-Depth Convolution (SPD-Conv). This compensates for the deficiency in utilizing fine-grained information during feature extraction. These efforts culminate in the development of the LSIM-YOLO model. Experimental results on the real dataset RTTS demonstrate a notable 3% improvement in average precision (AP), validating the feasibility and effectiveness of our improved detection framework.

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

Citation Download Citation

Xinwei Yao, Zitao Tu, Xinge Zhang, and Yuchen Zhang "Light source influence Matrix-YOLO: an object detection framework for low-visibility conditions", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762O (22 May 2024); https://doi.org/10.1117/12.3029425

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

Object detection

Image enhancement

Light sources

Detection and tracking algorithms

Image processing

Visibility

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years