A lightweight underwater fish image semantic segmentation model based on U‐Net
Zhenkai Zhang,
Wanghua Li,
Boon‐Chong Seet
Abstract:Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real‐time performance, and imprecise location segmentation in current methods, a novel lightweight U‐Net model is proposed. The proposed model acquires more segmentation details by applying a multiple‐input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi‐scal… Show more
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