Affiliation:
1. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
Abstract
Due to the increasing maturity of deep learning and remote sensing technology, the performance of object detection in satellite images has significantly improved and plays an important role in military reconnaissance, urban planning, and agricultural monitoring. However, satellite images have challenges such as small objects, multiscale objects, and complex backgrounds. To solve these problems, a lightweight object detection model named BSFCDet is proposed. First, fast spatial pyramid pooling (SPPF-G) is designed for feature fusion to enrich the spatial information of small targets. Second, a three-layer bidirectional feature pyramid network (BiFPN-G) is suggested to integrate the deep feature’s semantic information with the shallow feature’s spatial information, thus improving the scale adaptability of the model. Third, a novel efficient channel attention (ECAM) is proposed to reduce background interference. Last, a new residual block (Resblock_M) is constructed to balance accuracy and speed. BSFCDet achieves high detection performance while satisfying real-time performance, according to experimental results.
Funder
Natural Science Foundation of Jiangsu Province
National Natural Science Foundation of China
15th Six Talent Peaks Project in Jiangsu Province
Qing Lan Project
PAPD fund
Postgraduate Research & Practice Innovation Program of Jiangsu Province
Subject
General Earth and Planetary Sciences
Reference53 articles.
1. Remote sensing image super-resolution and object detection: Benchmark and state of the art;Wang;Expert Syst. Appl.,2022
2. Feature Split–Merge–Enhancement Network for Remote Sensing Object Detection;Ma;IEEE Trans. Geosci. Remote Sens.,2022
3. Viola, P., and Jones, M. (2001, January 8–14). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA.
4. Histograms of oriented gradients for human detection;Dalal;Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),2005
5. Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008, January 23–28). A discriminatively trained, multiscale, deformable part model. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Anchorage, AK, USA.