A Lightweight Object Detection and Recognition Method Based on Light Global-Local Module for Remote Sensing Images

Lightweight object detection and recognition models are extremely crucial for in-orbit applications, which is the most critical factor for whether deep learning-based object detection and recognition algorithms can be applied to remote sensing satellites for real-time or near real-time processing. G...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2023-01, Vol.20, p.1-1
Main Authors: Liu, Dongyang, Zhang, Junping, Li, Tong, Qi, Yunxiao, Wu, Yinhu, Zhang, Ye
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Computation
Computational modeling
Computer applications
Computing costs
Convolution
Deep learning
Detection
Feature extraction
global information extraction
Head
in-orbit applications
Information processing
Information retrieval
Light
Lightweight
Machine learning
Mathematical models
Modelling
Modules
Object detection
object detection and recognition
Object recognition
Parameters
Real time
Remote sensing
remote sensing images
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Summary:Lightweight object detection and recognition models are extremely crucial for in-orbit applications, which is the most critical factor for whether deep learning-based object detection and recognition algorithms can be applied to remote sensing satellites for real-time or near real-time processing. Global information is extremely important for object detection and recognition of remote sensing images. However, due to the high computational cost, the existing CNN-based lightweight models over-emphasize on the extraction of local information, while ignoring the global information. For this reason, we propose a lightweight object detection and recognition model (Lightweight Global-Local Detection, LGLDet) based on the especially light global modeling structure. In LGLDet, light global-local module (LGLM) is proposed to extract the global and local information. The LGLM consists of Point2Patch Non-Local (P2PNL), local branch and skip connection. Specifically, P2PNL is proposed to reduce the computation of global long-range dependency modeling. In addition, the feature fusion part and detection head are also designed in a lightweight way. In the experiments, the proposed method can achieve optimal performance with fewer parameters and lower computational complexity than existing CNN-based lightweight models and transformer-based lightweight models with similar parameters or computational complexity. The code will be released on the site of https://github.com/dyl96/LGLDet.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3292890