[5] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Columbus: IEEE, 2014: 580-587.

[6] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[7] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers [C]//European Conference on Computer Vision(ECCV). Cham: Springer, 2020: 213-229.

[8] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[R]. Los Alamos: arXiv Preprint, 2016: arXiv: 1512. 02325.

[9] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas: IEEE, 2016: 779-788.

[13] ROY A M, BHADURI J. A computer vision enabled damage detection model with improved YOLOv5 based on transformer prediction head[R]. Los Alamos: arXiv Preprint, 2023: arXiv: 2303. 04275.

[14] ZHANG Y L, GUO L H, WANG Z F, et al. Intelligent ship detection in remote sensing images based on multi-layer convolutional feature fusion[J]. Remote Sensing, 2020, 12(20): 3316.

[15] 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[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Vancouver: IEEE, 2023: 7464-7475.

[16] QI Y L, HE Y T, QI X M, et al. Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation[C]//IEEE/CVF International Conference on Computer Vision (ICCV). Paris: IEEE, 2023: 6070-6079.

[17] DING X H, ZHANG X Y, MA N N, et al. RepVGG: making VGG-style ConvNets great again[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville：IEEE, 2021: 13733-13742.

[18] WANG Q L, WU B G, ZHU P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 11534-11542.

[19] YU W H, ZHOU P, YAN S C, et al. InceptionNeXt: when inception meets convnext[R]. Los Alamos: arXiv Preprint, 2023: arXiv: 2303. 16900.

[20] LIU Z, MAO H Z, WU C-Y, et al. A ConvNet for the 2020s[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 11976-11986.

[21] MA S L, XU Y. MPDIoU: a loss for efficient and accurate bounding box regression[R]. Los Alamos: arXiv Preprint, 2023: arXiv: 2307. 07662.

[22] LIU D Y. TS2ANet: ship detection network based on transformer[J]. Journal of Sea Research, 2023, 195: 102415.

[24] WU W, LI X L, HU Z H, et al. Ship detection and recognition based on improved YOLOv7[J]. Computers, Materials and Continua, 2023, 76(1): 489-498.

[25] XU S L, WANG X X, LV W Y, et al. PP-YOLOE: an evolved version of YOLO[R]. Los Alamos: arXiv Preprint, 2022: arXiv: 2203. 16250.