[1] Huang J F. The study of crimping terminal process and reliability analysis[D], 20-22(2016).
[2] Li X J, Li B B, Yu X J. Online detection system of the wiring harness terminal[J]. Process Automation Instrumentation, 36, 65-68(2015).
[3] Lu B B, Liu L Q, Zheng Y M et al. A method for segmenting the microscopic cable harness image automatically[J]. Opto-Electronic Engineering, 43, 49-55(2016).
[5] Hou S M, Wang Y, Tang Q B et al. Crimp terminals sectional contour extraction based on partial differential equation and canny operator[J]. Journal of Shanxi University (Natural Science Edition), 40, 676-682(2017).
[6] Lu B B, Hu T Q, Liu T T. Variable exponential chromaticity filtering for microscopic image segmentation of wire harness terminals[J]. Optics and Precision Engineering, 27, 1894-1900(2019).
[7] Guo F, Wang Y Y. Application of terminal image measurement system based on human computer interaction and target detection[J]. Journal of Qiqihar University (Natural Science Edition), 37, 47-50, 61(2021).
[8] Jiang N, Zhou H Y, Yu F H. Review of computer vision based object counting methods[J]. Laser & Optoelectronics Progress, 58, 1400002(2021).
[10] Girshick R. Fast R-CNN[C], 1440-1448(2015).
[12] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[C], 2980-2988(2017).
[15] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).
[17] Zhao J M, Li X D, Li B S. Algorithm of sheep dense counting based on unmanned aerial vehicle images[J]. Laser & Optoelectronics Progress, 58, 2210013(2021).
[18] Ke B S, Li Y, Ren Z B et al. Deep learning-based detection method for mitosis in living cells[J]. Acta Optica Sinica, 41, 1511001(2021).
[20] Ming H Y, Chen C M, Liu G H et al. Improved counting algorithm for dense rebars based on RetinaNet[J]. Transducer and Microsystem Technologies, 39, 115-118(2020).
[23] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2999-3007(2017).