[1] [1] YuanW, Chin KS, HuaM, et al. Shape classification of wear particles by image boundary analysis using machine learning algorithms[J]. Mechanical Systems and SignalProcessing, 2016, 72/73: 346-358.

[2] [2] Peng YP, Wu TH, Cao GZ, et al. A hybrid search-tree discriminant technique for multivariate wear debris classification[J]. Wear, 2017, 392/393: 152-158.

[3] Stachowiak G P, Stachowiak G W, Podsiadlo P. Automated classification of wear particles based on their surface texture and shape features[J]. Tribology International, 41, 34-43(2008). http://www.sciencedirect.com/science/article/pii/S0301679X07000849

[4] Hong W, Cai W J, Wang S P et al. Mechanical wear debris feature, detection, and diagnosis: a review[J]. Chinese Journal of Aeronautics, 31, 867-882(2018). http://www.cnki.com.cn/Article/CJFDTotal-HKXS201805001.htm

[5] Raadnui S. Wear particle analysis: utilization of quantitative computer image analysis: a review[J]. Tribology International, 38, 871-878(2005). http://www.sciencedirect.com/science/article/pii/S0301679X05000915

[6] Terdenge L M, Heisel S, Schembecker G et al. Agglomeration degree distribution as quality criterion to evaluate crystalline products[J]. Chemical Engineering Science, 133, 157-169(2015). http://www.sciencedirect.com/science/article/pii/s0009250915001645

[7] Heisel S. Kova evi T, Briesen H, et al. Variable selection and training set design for particle classification using a linear and a non-linear classifier [J]. Chemical Engineering Science, 173, 131-144(2017).

[8] Mehle A, Kitak D, Podrekar G et al. In-line agglomeration degree estimation in fluidized bed pellet coating processes using visual imaging[J]. International Journal of Pharmaceutics, 546, 78-85(2018). http://www.ncbi.nlm.nih.gov/pubmed/29752979

[9] Wang J D, Cao Y J, Jiang X J et al. Agglomeration detection by acoustic emission (AE) sensors in fluidized beds[J]. Industrial & Engineering Chemistry Research, 48, 3466-3473(2009). http://pubs.acs.org/doi/pdf/10.1021/ie800324m

[10] Sheahan T, Briens L. Passive acoustic emissions monitoring of the coating of pellets in a fluidized bed: a feasibility analysis[J]. Powder Technology, 283, 373-379(2015). http://www.sciencedirect.com/science/article/pii/S0032591015004489

[11] Iacoviello F. Iacoviello D, di Cocco V, et al. Classification of ductile cast iron specimens based on image analysis and support vector machine[J]. Procedia Structural Integrity, 3, 283-290(2017).

[12] Ajdadi F R, Gilandeh Y A, Mollazade K et al. Application of machine vision for classification of soil aggregate size[J]. Soil and Tillage Research, 162, 8-17(2016). http://www.sciencedirect.com/science/article/pii/S0167198716300551

[13] Stachowiak G W, Podsiadlo P. Towards the development of an automated wear particle classification system[J]. Tribology International, 39, 1615-1623(2006). http://www.sciencedirect.com/science/article/pii/S0301679X06000302

[14] Zhang Y, Liu J J, Zhang L et al. Particle shape characterisation and classification using automated microscopy and shape descriptors in batch manufacture of particulate solids[J]. Particuology, 24, 61-68(2016). http://www.sciencedirect.com/science/article/pii/S1674200115000875

[15] Lee H. Chen Y P P. Cell morphology based classification for red cells in blood smear images[J]. Pattern Recognition Letters, 49, 155-161(2014). http://www.sciencedirect.com/science/article/pii/S0167865514001871

[16] Calderon C P, Daniels A L, Randolph T W. Deep convolutional neural network analysis of flow imaging microscopy data to classify subvisible particles in protein formulations[J]. Journal of Pharmaceutical Sciences, 107, 999-1008(2018). http://www.ncbi.nlm.nih.gov/pubmed/29269269

[17] Chaves D, Fernández-Robles L, Bernal J et al. Automatic characterisation of chars from the combustion of pulverised coals using machine vision[J]. Powder Technology, 338, 110-118(2018). http://www.onacademic.com/detail/journal_1000040424711110_c12c.html

[18] Vogado L H S, Veras R M S, Araujo F H D et al. . Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification[J]. Engineering Applications of Artificial Intelligence, 72, 415-422(2018).

[19] Raitoharju J, Riabchenko E, Ahmad I et al. Benchmark database for fine-grained image classification of benthic macroinvertebrates[J]. Image and Vision Computing, 78, 73-83(2018).

[20] Feng X Y, Mei W, Hu D S. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 38, 0615004(2018).

[21] Du J, Hu B L, Zhang Z F. Gastric carcinoma classification based on convolutional neural network and micro-hyperspectral imaging[J]. Acta Optica Sinica, 38, 0617001(2018).

[22] 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). http://www.ncbi.nlm.nih.gov/pubmed/27295650

[23] He K M, Gkioxari G, Dollár P et al. Mask R-CNN. [C]//2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2980-2988(2017).

[24] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[25] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection. [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 936-944(2017).

[26] 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). http://www.tandfonline.com/servlet/linkout?suffix=CIT0044&dbid=16&doi=10.1080%2F15481603.2018.1426091&key=10.1109%2FCVPR.2015.7298965

[27] Vala H J, Baxi A. A review on Otsu image segmentation algorithm[J]. International Journal of Advanced Research in Computer Engineering & Technology, 2, 387-389(2013).

[28] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 583-598(1991). http://ieeexplore.ieee.org/iel1/34/2845/00087344.pdf