Yajun Zhang1,*, Jianjun Yi1, Jiahao Zhang1, Yuanhao Chen1, Liang He2
Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 425-439, 2021, DOI:10.32604/iasc.2021.013795
Abstract Image recognition algorithms based on deep learning have been widely developed in recent years owing to their capability of automatically capturing recognition features from image datasets and constantly improving the accuracy and efficiency of the image recognition process. However, the task of training deep learning networks is time-consuming and expensive because large training datasets are generally required, and extensive manpower is needed to annotate each of the images in the training dataset to support the supervised learning process. This task is particularly arduous when the image scenes involve randomly stacked objects. The present work addresses this issue by developing a… More >
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