[2] GAO H, CHEN X N, SHI W Y. Analysis of the prevalence of blindness and major blinding diseases in China[J]. Chinese Journal of Ophthalmology, 55, 625-628(2019).

[3] HE H J. Research on the design of autonomous tracking shopping cart for visually impaired people[D](2022).

[4] FERRAND S, ALOUGES F, AUSSAL M. An electronic travel aid device to help blind people playing sport[J]. IEEE Instrumentation & Measurement Magazine, 23, 14-21(2020).

[5] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[6] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 521, 436-444(2015).

[7] GIRSHICK R, DONAHUE J, DARRELL T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

[8] GIRSHICK R. Fast R-CNN[C], 1440-1448(2015).

[9] 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, 39, 1137-1149(2017).

[10] HE K M, GKIOXARI G, DOLLÁR P et al. Mask R-CNN[C], 2980-2988(2017).

[11] LIU W, ANGUELOV D, ERHAN D et al. SSD: single shot MultiBox detector[C], 21-37(2016).

[12] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2999-3007(2017).

[13] REDMON J, DIVVALA S, GIRSHICK R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[14] MA Y J, SONG X F. Vehicle flow detection based on YOLO and embedded system[J]. Chinese Journal of Liquid Crystals and Displays, 34, 613-618(2019).

[15] ZUO C, QIAN J M, FENG S J et al. Deep learning in optical metrology: a review[J]. Light: Science & Applications, 11, 39(2022).

[16] LUO Y, ZHAO Y F, LI J X et al. Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. eLight, 2, 4(2022).

[17] SITU G. Deep holography[J]. Light: Advanced Manufacturing, 3, 278-300(2022).

[18] LI W T, ZHANG Y, MO J Q et al. Detection of pedestrian and agricultural vehicles in field based on improved YOLOv3-tiny[J]. Transactions of the Chinese Society for Agricultural Machinery, 51, 1-8, 33(2020).

[19] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[20] WOO S, PARK J, LEE J Y et al. CBAM: convolutional block attention module[C], 3-19(2018).

[21] LIU L, GOU J N. Research on detection method of railway intrusion obstacles based on YOLOv4[J]. Journal of Railway Science and Engineering, 19, 528-536(2022).

[22] WANG H J, HE Y, ZHOU H et al. Detection and recognition of railway track obstacles based on YOLOv5 network model[J]. China Storage and Transport, 86-87(2022).

[23] GE Z, LIU S T, WANG F et al. YOLOX: Exceeding YOLO series in 2021[J/OL](2021).

[24] WANG C Y, LIAO H Y M, WU Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[25] LIU S, QI L, QIN H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[26] LUO Y H, CAO X, ZHANG J T et al. CE-FPN: enhancing channel information for object detection[J]. Multimedia Tools and Applications, 81, 30685-30704(2022).

[27] MISRA D, NALAMADA T, ARASANIPALAI A U et al. Rotate to attend: convolutional triplet attention module[C], 3138-3147(2021).

[28] REZATOFIGHI H, TSOI N, GWAK J Y et al. Generalized intersection over union: a metric and a loss for bounding box regression[C], 658-666(2019).

[29] ZHENG Z H, WANG P, REN D W et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 52, 8574-8586(2022).

[30] GEVORGYAN Z. SIoU Loss: More powerful learning for bounding box regression[J/OL](2022).

[31] ZHANG H Y, WANG Y, DAYOUB F et al. VarifocalNet: an IoU-aware dense object detector[C], 8510-8519(2021).