[1] Hou L P. Study of dynamic wetting effect and its corresponding dynamic contact angle based on laser reflection method[D], 14-16(2018).

[2] Goclawski J, Urbaniak-Domagala W. The method of solid-liquid contact angle measurement using the images of sessile drops with shadows on substratum[C], 135-140(2007).

[3] Li L, Kang W, Ye D T. A contact angle measurement method for the droplets in EWOD-based chips[C], 1071-1075(2007).

[4] Zhang P, Wan Y J, Zhou Y L. A new boundary extraction algorithm and its application in the contact angle measurement[J]. Journal of East China University of Science and Technology, 40, 746-751, 762(2014).

[5] Wang X H, Li J J, Yang W et al. Measurement on contact angles based on image process[J]. Optoelectronic Technology, 31, 14-19(2011).

[6] Xiong Y, Jia Z H, Cai X S. Measurement of droplet contact angle with image analyzing method[J]. Metrology & Measurement Technology, 30, 9-11(2010).

[7] Ning Q, Zhu Z Q, Lü X T et al. Determine the surface tension and contact angle of drop by image processing method[J]. Chinese Journal of Space Science, 28, 74-79(2008).

[8] Ríos-López I, Karamaoynas P, Zabulis X et al. Image analysis of axisymmetric droplets in wetting experiments: a new tool for the study of 3D droplet geometry and droplet shape reconstruction[J]. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 553, 660-671(2018).

[9] Wang H, Wang J, Cao Z L. Water contact angle calculation method based on faster RCNN[J]. Laser & Optoelectronics Progress, 61, 0812002(2024).

[10] Hou X H, Jin G D, Tan L N. Survey of ship detection in SAR images based on deep learning[J]. Laser & Optoelectronics Progress, 58, 0400005(2021).

[11] Sheng X Q, Li S B, Qu J Y et al. 3D object detection algorithm based on improved YOLOv5[J]. Laser & Optoelectronics Progress, 61, 1812006(2024).

[12] Hao B Q, Fan Y G, Song Z H. Deep transfer learning-based pulsed eddy current thermography for crack defect detection[J]. Acta Optica Sinica, 43, 0415002(2023).

[13] Varghese R, Sambath M. YOLOv8: a novel object detection algorithm with enhanced performance and robustness[C](2024).

[14] Cheng S, Li J R, Wang Z Q et al. Based on YOLOv8 lightweight underwater optical image recognition algorithm[J/OL]. Laser and Optoelectronics Progress, 62, 0437008(2025).

[15] Zhang Y, Ma J, Cui J W et al. Rotation target detection algorithm for remote sensing image using attention mechanism[J]. Laser & Optoelectronics Progress, 59, 2415005(2022).

[16] Llerena J M, Zeni L F, Kristen L N et al. Gaussian bounding boxes and probabilistic intersection-over-union for object detection[EB/OL]. https://arxiv.org/abs/2106.06072v1

[17] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[18] Wang C Y, Mark Liao H Y, Wu Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[19] Jocher G. YOLOv5 by ultralytics[EB/OL]. https://github.com/ultralytics/yolov5

[20] Shu A J, Zhang Y C. Lightweight weed detection model based on improved YOLOv8[J]. Software Engineer, 27, 18-22(2024).

[21] Li H L, Li J, Wei H B et al. Slim-neck by GSConv: a lightweight-design for real-time detector architectures[J]. Journal of Real-Time Image Processing, 21, 62(2024).

[22] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

[23] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://doi.org/10.48550/arXiv.1412.6980

[24] Xu Y C, Fu M T, Wang Q M et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1452-1459(2021).

[25] Ding J, Xue N, Long Y et al. Learning RoI transformer for oriented object detection in aerial images[C], 2844-2853(2019).

[26] Xie X X, Cheng G, Wang J B et al. Oriented R-CNN for object detection[C], 3500-3509(2021).

[27] Khanam R, Hussain M. YOLOv11: an overview of the key architectural enhancements[EB/OL]. https://doi.org/10.48550/arXiv.2410.17725