Enhanced marine fish small sample image recognition with RVFL in Faster R-CNN model

Abstract

Addressing the challenge of insufficient underwater marine life images leading to diminished accuracy in existing target recognition models, this paper introduces Random Vector Functional Link (RVFL), a suitable model for small sample training, to reduce the dependency of traditional visual recognition models on data volume by replacing the Softmax classifier in the Faster Region-Based Convolutional Neural Network (R-CNN) model. The RVFL network assigns random weights to enhance the model's adaptability to diverse data distributions, thereby improving its generalization performance. Furthermore, the RVFL model incorporates multiple nonlinear activation functions, thereby enhancing the neural network's representational capacity for high-dimensional data. The application of the proposed model to the task of marine fish small sample target recognition demonstrates its effectiveness in mitigating the low accuracy issue caused by data scarcity. It significantly improved recognition accuracy and offered new insights into addressing underwater small sample image recognition challenges.