The text detection algorithm for agricultural materials image based on Ghost module and its application

doi:10.14088/j.cnki.issn0439-8114.2024.08.011

Abstract

Abstract: In response to problems such as slow detection speed of text in agricultural materials image and lack of mobile applications, based on the agricultural materials image dataset, a Ghost module-based text detection algorithm for agricultural materials image was proposed, which improved the DB network, used the MobileNetv2 network to extract the base features, introduced a multi-scale feature fusion module to obtain feature fusion between multiple layers, and used a differentiable binary post-processing algorithm to predict the text, making it possible to quickly detect the text in agricultural materials image. The accuracy of the algorithm on the agricultural materials image dataset was basically up to the standard of mainstream algorithms, with a detection speed of 18.6 img/s and a census count of 2.99 M, with lightweight features, and the algorithm was deployed to mobile devices and ran successfully.

Key words: agricultural materials image, text detection, text recognition, Ghost module

CLC Number:

TP391.1

YIN Chang-shan, YANG Lin-nan, LUO Shuang. The text detection algorithm for agricultural materials image based on Ghost module and its application[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(8): 61-65.

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