Automatic fish classification played an essential role in the fisheries sector, particularly in underwater environments where visual quality was often degraded. This study addressed challenges related to low-contrast underwater images and limited dataset conditions by integrating Contrast Limited Adaptive Histogram Equalization (CLAHE) with a VGG16-based transfer learning model with regularization approaches including L1, L2, and Dropout. The dataset consisted of multiple fish species, including Bream, Sea Bass, Horse Mackerel, Red Mullet, and Black Sea Sprat. To enhance dataset diversity, data augmentation was performed using geometric transformations such as rotation, flipping, cropping/resizing, translation, shearing, and zooming. The dataset was divided into training (70%, 18,900 images), validation (20%, 5,400 images), and testing (10%, 2,700 images). Experimental results showed that the VGG16-CLAHE-Dropout model achieved the best overall performance, with training, validation, and testing accuracies of 99.15%, 98.37%, and 97.07%, respectively. CLAHE was implemented using a clip limit of 2.0 and a tile grid size of 8×8 to enhance image contrast, while the model was optimized using the Adam optimizer with a learning rate of 0.0001 and a batch size of 32. These findings demonstrated that combining contrast enhancement with appropriate regularization techniques significantly improved deep learning performance for underwater fish species classification.
Noprisson et al. (Thu,) studied this question.