[1] WANG C-Y, BOCHKOVSKIT A, LIAO H-Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023. doi: 10.1109/CVPR52729.2023.00721.

[2] WANG C-Y, YEH I-H, LIAO H-Y M. You only learn one representation: unified network for multiple tasks [R]. Los Alamos: arXiv Preprint, 2021: arXiv: 2105. 04206.

[3] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in 2021 [R]. Los Alamos: arXiv Preprint, 2021: arXiv: 2107. 08430．

[6] MOU X A, LEI S, ZHOU X. YOLO-FR: a YOLOv5 infrared small target detection algorithm based on feature reassembly sampling method [J]. Sensors, 2023, 23(5): 2710.

[10] LIU T-Y, GOYAL P, GIRSHICK R, et al. Focal Loss for dense object detection [C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017. doi: 10.1109/ICCV.2017.324.

[11] WEBER M, FRST M, ZLLNER J M. Automated Focal Loss for image based object detection [C]//2020 IEEE Intelligent Vehicles Symposium (IV). Las Vegas: IEEE, 2020. doi: 10.1109/IV47402.2020.9304830.

[12] LENG Z Q, TAN M X, LIU C X, et al. PolyLoss: a polynomial expansion perspective of classification loss functions [R]. Los Alamos: arXiv Preprint, 2022: arXiv: 2204. 12511.

[13] CHAROENPHAKDEE N, VONGKULBHISAL J, CHAIRAT-ANAKUL N, et al. On Focal Loss for class-posterior probability estimation: a theoretical perspective [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021. doi: 10.1109/CVPR46437.2021.00516.

[14] LI B, YAO Y Q, TAN J R, et al. Equalized Focal Loss for dense long-tailed object detection [C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022. doi: 10.1109/CVPR52688.2022.00686.

[15] MUHAMMAD M B, YEASIN M. Eigen-CAM: class activation map using principal components [C]//2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020. doi: 10.1109/IJCNN48605.2020.9206626.

[16] DUAN K W, BAI S, XIE L X, et al. CenterNet: keypoint triplets for object detection [C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019. doi: 10.1109/ICCV.2019.00667.

[17] SUN P Z, ZHANG R F, JIANG Y, et al. Sparse R-CNN: end-to-end object detection with learnable proposals [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021. doi: 10.1109/CVPR46437.2021.01422.

[18] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020. doi: 10.1109/CVPR42600.2020.01079.

[19] ZHU B J, WANG J F, JIANG Z K, et al. AutoAssign: differentiable label assignment for dense object detection [R]. Los Alamos: arXiv Preprint, 2020: arXiv: 2007. 03496.

[20] CHEN Q, WANG Y M, YANG T, et al. You only look one-level feature [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021. doi: 10.1109/CVPR46437.2021.01284.

[21] ZHU X Z, SU W J, LU L W, et al. Deformable DETR: deformable transformers for end-to-end object detection [R]. Los Alamos: arXiv Preprint, 2020: arXiv: 2010. 04159.