YOLO-GC: A Lightweight Model for Remote Sensing Image Object Detection

Zhongren Liang

doi:10.54691/ask7j588

Authors

Zhongren Liang

DOI:

https://doi.org/10.54691/ask7j588

Keywords:

Remote Sensing Images; Object Detection; YOLOv8; Lightweight; GhostConv; CBAM.

Abstract

Remote sensing images are crucial national strategic resources. However, existing high-performance object detection models for remote sensing images typically suffer from high computational complexity and large parameter sizes. To reduce computational demands while maintaining detection accuracy for multi-scale targets, this study proposes YOLO-GC, a lightweight object detection model for remote sensing images. First, the conventional Conv modules in the YOLOv8 backbone network are replaced with GhostConv, and the C2f module is enhanced into a C2f-GhostConv structure integrating Ghost operations, significantly reducing model complexity. Meanwhile, a CBAM attention layer is incorporated into the backbone network, effectively improving feature extraction capability without increasing computational overhead. Experimental results on the NWPU VHR-10 remote sensing object detection dataset demonstrate that YOLO-GC achieves a computational complexity of 21.4 GFLOPs, reducing the baseline YOLOv8s' complexity by 7.4 GFLOPs, while maintaining a mAP@50 of 0.922-only 0.011 lower than the baseline. YOLO-GC achieves a superior balance between accuracy and efficiency, significantly enhancing its potential for deployment on resource-constrained edge platforms.

Downloads

Download data is not yet available.

References

[1] Tang W J. Research Status of Remote Sensing Image Classification Based on Deep Learning[J]. Computer Science and Application, 2024, 14(8): 101-109. DOI: 10.12677/csa.2024.148179

[2] Liu X B, Liu P, Cai Z H, Qiao Y L, Wang L, Wang M. Advances in Optical Remote Sensing Image Object Detection Based on Deep Learning[J]. Acta Automatica Sinica, 2021, 47(9): 2078-2089. DOI: 10.16383/j.aas.c190455

[3] Cao X M. Vehicle Detection Based on Multi-Image Feature Pyramid[D]. Beijing Jiaotong University, 2016. DOI:10.7666/d.Y3124193.

[4] Zhang G M, Zhang S, Chu J. A Novel Target Detection Method Based on Local Contour Features[J]. Acta Automatica Sinica, 2014, 40(10): 2346-2355. DOI:10.3724/SP.J.1004.2014.02346.

[5] Lu, D,Weng, Q,SanchezHernandez, Carolina,et. Support vector machines in remote sensing: A review[J]. 2011.

[6] Mariana,Belgiu,Lucian,et al.Random forest in remote sensing: A review of applications and future directions[J].Isprs Journal of Photogrammetry & Remote Sensing, 2016.DOI:10.1016/j.isprsjprs.2016.01.011.

[7] Felzenszwalb P F , Mcallester D A , Ramanan D .A discriminatively trained, multiscale, deformable part model[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition.IEEE, 2008.DOI:10.1109/CVPR.2008.4587597.

[8] Girshick R , Donahue J , Darrell T ,et al.Region-Based Convolutional Networks for Accurate Object Detection and Segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 38(1):142-158.DOI:10.1109/TPAMI.2015.2437384.

[9] Girshick R .Fast R-CNN[J].Computer Science, 2015.DOI:10.1109/ICCV.2015.169.

[10] Ren S , He K , Girshick R ,et al.Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.DOI:10.1109/TPAMI.2016.2577031.

[11] Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788

[12] Wei L , Dragomir A , Dumitru E ,et al.SSD: Single Shot MultiBox Detector[J].Springer, Cham, 2016.DOI:10.1007/978-3-319-46448-0_2.

[13] Duan K , Bai S , Xie L ,et al.CenterNet: Keypoint Triplets for Object Detection[J].University of Chinese Academy of Sciences; University of Oxford; Huawei Noah's Ark Lab; Huawei Noah’s Ark Lab[2025-07-09].DOI:10.1109/ICCV.2019.00667.

[14] Ju M R, Luo H B, Wang Z B, et al. Improved YOLO V3 Algorithm and Its Application in Small Target Detection[J]. Acta Optica Sinica, 2019, 39(7): 0715004. DOI:10.3788/AOS201939.0715004.

[15] Wang X K, Jiang H X, Lin K Y. Ship Detection in Remote Sensing Images Based on Improved YOLO Algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 8. DOI:10.13700/j.bh.1001-5965.2019.0394.

[16] Tang J Y, Tang C H. Remote Sensing Image Object Detection Algorithm Based on Rotated Bounding Box and Attention Mechanism[J]. Electronic Measurement Technology, 2021, 44(13): 7. DOI:10.19651/j.cnki.emt.2106740.

[17] Zhang S W, Shi W Y, Zhang S Q, et al. Small Target Detection in Remote Sensing Images Based on Weighted Receptive Field and Cross-Layer Fusion[J]. Electronic Measurement Technology, 2023, 46(18): 129-138.

[18] Yan J H, Zhang K, Shi T J, et al. Weak and Small Ground Target Detection in Remote Sensing Images by Fusing Multi-Level Features[J]. Chinese Journal of Scientific Instrument, 2022(3): 221-229.

[19] Karim S , Zhang Y , Yin S ,et al.A brief review and challenges of object detection in optical remote sensing imagery[J].Multiagent and Grid Systems, 2020, 16(3):227-243.DOI:10.3233/MGS-200330.

[20] Han,Junwei,Cheng,et al.A survey on object detection in optical remote sensing images[J].ISPRS journal of photogrammetry and remote sensing, 2016, 117(Jul.):11-28.

[21] Ma C W, Zhang H, Ma X M, et al. Lightweight Wheat Disease Detection Method Based on Improved YOLOv8[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(5). DOI:10.11975/j.issn.1002-6819.202309211.

[22] Varghese R , Sambath M .YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness[C]//2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS).0[2025-07-09].DOI:10.1109/ADICS58448.2024.10533619.

[23] Han K , Wang Y , Tian Q ,et al.GhostNet: More Features From Cheap Operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2020.DOI:10.1109/CVPR42600.2020.00165.

[24] Woo S , Park J , Lee J Y ,et al.CBAM: Convolutional Block Attention Module[J].Springer, Cham, 2018.DOI:10.1007/978-3-030-01234-2_1.

[25] Ma N , Zhang X , Zheng H T ,et al.ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design[J].Springer, Cham, 2018.DOI:10.1007/978-3-030-01264-9_8.

[26] Daquan Z , Hou Q , Chen Y ,et al.Rethinking Bottleneck Structure for Efficient Mobile Network Design[J]. 2020.DOI:10.1007/978-3-030-58580-8_40.