[1] Yong Y W, Zhang W W, Wang G et al. Research on extraction method of molten pool morphology in laser cladding based on deep learning[J]. Applied Laser, 44, 34-41(2024).

[2] Peng J, Xu H Q, Wang X X et al. Study on the dynamic behavior of molten pool in laser welding process[J]. Transactions of the China Welding Institution, 44, 1-7, 129(2023).

[3] Huang Y H, Fan X A, Zhang Y X et al. Online detection of root hump in laser-MIG hybrid welding based on invariable moment characteristics of molten pool image[J]. Chinese Journal of Lasers, 50, 1202108(2023).

[4] Xie G M, Wang S H, Zhang Y Q et al. Laser welding depth monitoring method based on optical coherence tomography[J]. Acta Optica Sinica, 43, 1114002(2023).

[5] Hong Y X, Yang M X, Du D et al. Unstable state vision detection of molten pool during aluminum alloy climbing-TIG welding[J]. Transactions of the China Welding Institution, 42, 8-13(2021).

[6] Miao L G, Xing F, Shi J J et al. Melt pool size extraction method in laser directed energy deposition based on edge gradient search[J]. Materials Reports, 38, 217-223(2024).

[7] Ma X F, Xia P, Liu H S et al. Deep learning detection method of all-position welding pool[J]. Journal of Mechanical Engineering, 59, 272-283(2023).

[8] Yang L, Song S A, Fan J F et al. An automatic deep segmentation network for pixel-level welding defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5003510(2021).

[9] Zhang K, Li Y W, Liang J Y et al. Practical blind image denoising via Swin-Conv-UNet and data synthesis[J]. Machine Intelligence Research, 20, 822-836(2023).

[10] Cui Y T, Jiang C, Wang L M et al. MixFormer: end-to-end tracking with iterative mixed attention[C], 13598-13608(2022).

[11] Cheng B W, Misra I, Schwing A G et al. Masked-attention mask transformer for universal image segmentation[C], 1280-1289(2022).

[12] Xu T F, Wang Y, Shi G K et al. Research progress in fundamental architecture of deep learning-based single object tracking method[J]. Acta Optica Sinica, 43, 1510003(2023).

[13] Carion N, Massa F, Synnaeve G et al. End-to-end object detection with transformers[M]. Computer vision‒ECCV 2020, 12346, 213-229(2020).

[14] Zheng S X, Lu J C, Zhao H S et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C], 6877-6886(2021).

[15] Vaswani A, Shazeer N M, Parmar N et al. Attention is all you need[EB/OL]. https:∥arxiv.org/abs/1706.03762

[16] Cheng B W, Schwing A G, Kirillov A. Per-pixel classification is not all you need for semantic segmentation[EB/OL]. http:∥arxiv.org/abs/2107.06278v2

[17] Shu C Y, Liu Y F, Gao J F et al. Channel-wise knowledge distillation for dense prediction[C], 5291-5300(2021).

[18] Wu G Y, Yuan Z Q, Liang Y M. Unsupervised denoising of retinal OCT images based on deep learning[J]. Acta Optica Sinica, 43, 2010002(2023).

[19] Yan B, Peng H W, Fu J L et al. Learning spatio-temporal transformer for visual tracking[C], 10428-10437(2021).

[20] Li B, Wu W, Wang Q et al. SiamRPN: evolution of Siamese visual tracking with very deep networks[C], 4277-4286(2019).

[21] Chen B, He S, Liu J et al. Weld structured light image segmentation based on lightweight DeepLab v3+ Network[J]. Chinese Journal of Lasers, 50, 0802105(2023).

[22] Tan M X, Le Q V. EfficientNetV2: smaller models and faster training[EB/OL]. https:∥arxiv.org/abs/2104.00298

[23] Radosavovic I, Kosaraju R P, Girshick R et al. Designing network design spaces[C], 10425-10433(2020).

[24] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C]. Republic of Korea, 1314-1324(2019).

[25] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]. Computer vision–ECCV 2018, 11218, 122-138(2018).

[26] Gou J N, Du S S, Wang S D et al. Insulator self-explosion detection in transmission line based on CenterNet fusing lightweight features[J]. Journal of Beijing University of Aeronautics and Astronautics, 50, 2161-2171(2024).

[27] Hao S, Wu Y Q, Ma X et al. Photovoltaic hot spot detection method incorporating knowledge distillation and attention mechanisms[J]. Optics and Precision Engineering, 31, 3640-3650(2023).

[28] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[29] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[30] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Computer vision‒ECCV 2018, 11211, 833-851(2018).