[1] Shukun LIU, Zhanliang LI, Ningning SUN. Corrosion and characterization of defects in crystalline silicon. China Petroleum and Chemical Standard and Quality, 31, 43-48(2011).

[2] B GALLIEN, S BAILLY, T DUFFAR. Comparative study of dislocation density characterizations on silicon. Crystal Research and Technology, 52, 201600224.(2017).

[3] S WOO, M BERTONI, K CHOI et al. An insight into dislocation density reduction in multicrystalline silicon. Solar Energy Materials and Solar Cells, 155, 88-100(2016).

[4] D B NEEDLEMAN, H CHOI, D M POWELL et al. Rapid dislocation-density mapping of as-cut crystalline silicon wafers. Physica Status Solidi-Rapid Research Letters, 7, 1041-1044.(2013).

[5] Meng LI, Junfei LIU. Generation principle and detection methods on straight pulling silicon crystal dislocation and micro defects. Physics Bulletin, 2, 121-123(2013).

[6] Hongzhi ZHOU, Gan YU. Research on pedestrian detection technology based on the SVM classifier trained by HOG and LTP features. Future Generation Computer Systems, 125, 604-615(2021).

[7] Xuan LI, Zhou YANG, Xinyu TAO et al. Particle image detection based on Mask R-CNN combined with edge segmentation. Journal of Applied Optics, 43, 93-103(2022).

[8] D JIANG, G LI, C TAN et al. Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model. Future Generation Computer Systems, 123, 94-104(2021).

[9] S SHIN, H HAN, S H LEE. Improved YOLOv3 with duplex FPN for object detection based on deep learning. https://journals.sagepub.com/doi/10.1177/0020720920983524

[10] Z YU, Y SHEN, C SHEN. A real-time detection approach for bridge cracks based on YOLOv4-FPM. Automation in Construction, 122, 103514(2021).

[11] Z WANG, L WU, T LI et al. A smoke detection model based on improved YOLOv5. Mathematics, 10, 1-13.(2022).

[12] M F JUBAYER, MJA SOEB, M K PAUL et al. Detection of mold on the food surface using YOLOv5. Current Research in Food Science, 4, 724-728(2021).

[13] Zhiyong TAO, Fuyan DU, Xiaokui REN et al. , Defect detection of solar cells based on T-VGG. Acta Energiae Solaris Sinica, 43, 145-151(2022).

[14] Y Z FU, X LI, X MA et al. Deep-learning-based defect evaluation of mono-like cast silicon wafers. Photonics, 8, 426.(2021).

[15] H REZATOFIGHI, N TSOI, J GWAK et al. Generalized Intersection over union: a metric and a loss for bounding box regression. http://arxiv.org/abs/1902.09630

[16] Z NIU, G ZHONG, H YU. A review on the attention mechanism of deep learning. Neurocomputing, 452, 48-62(2021).

[17] Z ZHENG, P WANG, W LIU et al. Distance-IoU Loss: faster and better learning for bounding box regression. http://arxiv.org/abs/1911.08287