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

• 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
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3292890

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.