Naive Bayesian fusion based deep learning networks for multisegmented classification of fishes in aquaculture industries

Abstract

Fish classification is an essential requirement for biomass estimation, disease identification, and quality analysis. In aquaculture industries, fish classification is carried out in the processing unit. The fishes are out of water, subjecting them to structural deformation and orientation misalignments, makes classification challenging. A multisegmented fish classification technique using deep learning networks with naive Bayesian type fusion is proposed in this work to address these challenges. Fish images are acquired using an overhead camera. The fish head is identified by observing a minimal convexity deficiency region to facilitate segmentation. A multi-stage exhaustive enumerative optimization method is used to adjust the orientation, which can minimize unwanted background region in the image segment. Fish head, scales, and body are segmented from the fish image. For each fish segment, AlexNet is trained by using the transfer learning approach. A naive Bayesian fusion layer is introduced to fuse these trained deep learning networks and enhance classification accuracy. Experimental results illustrate a classification accuracy of 98.64% for ‘Fish-Pak’ image dataset with six different fish species and 98.94% for BYU fish dataset with four species. Comparative analysis with standard networks and ablation study demonstrates the accuracy and robustness of the proposed fusion architecture, respectively. Various fusion layers have also been analyzed, and observations illustrate the accuracy of the proposed NBC layer. Significant improvements in other classification performance metrics were also observed.