Abstract
In response to the poor performance of object detection in remote sensing images under complex backgrounds, this paper proposes a new object detection algorithm model called YOLO-L based on the YOLOv5 algorithm. This algorithm model constructs a novel backbone network called BottleCSP, which refreshs the C3 module and SPP module in the original YOLOv5 backbone network. It integrates feature maps of different sizes to beef up contextual information, thereby better extracting deep linguistic information from images. The BottleCSP backbone network solves the problem of poor performance in handling images with small target recognition by customizing and adjusting the original Backbone backbone network. Additionally, the CoordAttention attention mechanism is inserted in both the Backbone backbone network and the Head prediction head, which helps the network to more accurately locate the objects of interest and enhance the model’s perception ability in different regions of the image. This paper conducts experiments on the RSOD public dataset, and compared with the original YOLOv5 algorithm and newer detection methods such as DC-SPP-YOLO, MRFF-YOLO, and YOLOv5-slight, the mAP (mean average precision) is improved by 12.3%, 16.22%, 4.47%, and 7.67%, respectively. This effectively improves the recognition performance of images with small targets.
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References
Cheng, Y., et al.: A multi-feature fusion and attention network for multi-scale object detection in remote sensing images. Remote Sens. 15(8), 2096 (2023)
Zhong, Y., Wang, J., Zhao, J.: Adaptive conditional random field classification framework based on spatial homogeneity for high-resolution remote sensing imagery. Remote Sens. Lett. 11(6), 515–524 (2020)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
SunX, X., et al.: Fair1m: a benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery. ISPRS J. Photogramm. Remote. Sens. 184, 116–130 (2022)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Qu, Z., Zhu, F., Qi, C.: Remote sensing image target detection: improvement of the YOLOV3 model with auxiliary networks. Remote Sens. 13(19), 3908 (2021)
Zheng, Z., Liu, Y., Pan, C., Li, G.: Application of improved YOLOv3 in aircraft recognition of remote sensing images. Electron. Opt. Control. 26(4), 28–32 (2019)
Wu, D., Lv, S., Jiang, M., Song, H.: Using channel pruning-based yolo v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments. Comput. Electron. Agric. 178, 105742 (2020)
Tan, S., Bie, X., Lu, G., Tan, X.: Real-time detection of personnel mask placement based on the YOLOv5 network model. Laser J. 147–150 (2021)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Hou, T., Jiang, Y.: Application of improved YOLOv4 in remote sensing aircraft target detection. Comput. Eng. Appl. 12(57), 224–230 (2021)
Yasir, M., et al.: Multi-scale ship target detection using SAR images based on improved YOLOv5. Front. Mar. Sci. 9, 1086140 (2023)
Wu, Z., Su, L., Huang, Q.: Decomposition and completion network for salient object detection. IEEE Trans. Image Process. 30, 6226–6239 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713–13722 (2021)
Wen, G., Li, S., Liu, F., Luo, X., Er, M.J., Mahmud, M., Wu, T.: YOLOV5s-CA: a modified yolov5s network with coordinate attention for underwater target detection. Sensors 23(7), 3367 (2023)
Körez, A., Barışçı, N., Çetin, A., Ergün, U.: Weighted ensemble object detection with optimized coefficients for remote sensing images. ISPRS Int. J. Geo Inf. 9(6), 370 (2020)
Xia, G.S., et al.: DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974–3983 (2018)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Huang, Z., Wang, J., Fu, X., Yu, T., Guo, Y., Wang, R.: DC-SPP-YOLO: dense connection and spatial pyramid pooling based YOLO for object detection. Inf. Sci. 522, 241–258 (2020)
Xu, D., Wu, Y.: MRFF-YOLO: a multi-receptive fields fusion network for remote sensing target detection. Remote Sens. 12(19), 3118 (2020)
Lang, L., Xu, K., Zhang, Q., Wang, D.: Fast and accurate object detection in remote sensing images based on lightweight deep neural network. Sensors 21(16), 5460 (2021)
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Yinghe, W., Wenjun, L., Jiangbo, W. (2024). YOLO-L: A YOLO-Based Algorithm for Remote Sensing Image Target Detection. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-031-53401-0_20
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