Comparative Analysis VGG16 Vs MobileNet Performance for Fish Identification

Abstract

This research aims to conduct a comparative evaluation of the efficacy of two neural network architectures in the field of fish identification through the utilization of supervised learning techniques. The evaluation of VGG16 and MobileNet, which are prominent deep learning architectures, has been conducted about their speed, accuracy, and efficiency in resource utilization. To assess the classification performance of both architectures, we employed a dataset encompassing diverse fish categories. The findings indicated that the VGG16 model demonstrated superior accuracy in fish classification, albeit due to increased computational time and resource utilization. On the contrary, MobileNet exhibits enhanced speed and efficiency, albeit at a marginal cost to its accuracy. The findings of this study have the potential to inform the selection of deep learning models for fish recognition scenarios, considering the specific requirements of the task, such as prioritizing accuracy or efficiency. The findings mentioned above offer significant insights that can be utilized in the advancement of Artificial Intelligence (AI)-based applications within the domains of fisheries resource management and environmental monitoring. These applications specifically necessitate precise and effective fish recognition capabilities. The comparison findings indicate that the accuracy achieved by VGG16 was 0.99, whereas MobileNet also attained an accuracy of 0.99.

Keywords

Accuracy ; Fish Classification ; Supervised Learning ; MobileNet ; VGG16