Abstract
Small ship detection is widely used in marine environment monitoring, military applications and so on, and it has gained increasing attentions both in industry and academia. In this paper, we propose an effective small ship detection algorithm with enhanced-YOLOv7. Specifically, to reduce the feature loss of small ships and the impact of marine environment, we firstly design a small object-aware feature extraction module by considering both small-scale receptive fields and multi-branch residual structures. In addition, we propose a small object-friendly scale-insensitive regression scheme, to strengthen the contributions of both bounding box distance and difficult samples on regression loss as well as further increase learning efficiency of small ship detection. Moreover, based on the formulated penalty model, we design a geometric constraint-based Non-Maximum Suppression (NMS) method, to effectively decrease small ship detection omission rate. Finally, extensive experiments are implemented, and corresponding results confirm the effectiveness of the proposed algorithm.
Supported by the National Science Fund of China under Grant 62006119.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bai, Y., Zhang, Y., Ding, M., Ghanem, B.: SOD-MTGAN: small object detection via multi-task generative adversarial network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 210–226. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_13
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Deng, C., Wang, M., Liu, L., Liu, Y., Jiang, Y.: Extended feature pyramid network for small object detection. IEEE Trans. Multimedia 24, 1968–1979 (2021)
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578 (2019)
Gevorgyan, Z.: SIoU Loss: more powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740 (2022)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
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)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Liu, W., et al.: 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
Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., Chao, D.: Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network. Remote Sens. 12(9), 1432 (2020)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. Advances in neural information processing systems, vol. 28 (2015)
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)
Shao, Z., Wu, W., Wang, Z., Du, W., Li, C.: SeaShips: a large-scale precisely annotated dataset for ship detection. IEEE Trans. Multimedia 20(10), 2593–2604 (2018)
Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)
Tong, Z., Chen, Y., Xu, Z., Yu, R.: Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv preprint arXiv:2301.10051 (2023)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
Wang, J., Xu, C., Yang, W., Yu, L.: A normalized gaussian Wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389 (2021)
Yang, C., Huang, Z., Wang, N.: QueryDet: cascaded sparse query for accelerating high-resolution small object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13668–13677 (2022)
Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146–157 (2022)
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU Loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12993–13000 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, J., Ding, N., Gong, C., Jin, Z., Li, G. (2024). Effective Small Ship Detection with Enhanced-YOLOv7. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_21
Download citation
DOI: https://doi.org/10.1007/978-981-99-8549-4_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8548-7
Online ISBN: 978-981-99-8549-4
eBook Packages: Computer ScienceComputer Science (R0)