Bay Lobsters Moulting Stage Analysis Based on High-Order Texture Descriptor

Abstract

In this paper, we introduce the world's first method to automatically classify the moulting stage of Bay lobsters, formally known as Thenus orientális, in a controlled environment. Our classification approach only requires top view images of exoskeleton of bay lobsters. We analyzed the texture of exoskeleton to categorize into normal, moulting stage, and freshly moulted classes. To meet the efficiency and robustness requirements of production platform, we leverage traditional approach such as Local Binary Pattern and Local Derivative Pattern with enhanced encoding scheme for underwater imagery. We also build a dataset of 315 bay lobster images captured at the controlled under water environment. Experimental results on this dataset demonstrated that the proposed method can effectively classify bay lobsters with a high accuracy.