Underwater Optical Fish Classification System By Means Of Robust Feature Decomposition And Analysis Using Multiple Neural Networks

Live fish recognition and classification play a pivotal role in underwater understanding, because it help scientists to control the subsea inventory in order to aid fishery management. However, despite technological progress, fish recognition systems still have many limitations on observing fish. Difficulties in visualizing optical images can arise due to external attenuation, scattering properties of water. Optical underwater imaging systems can also have detection problems such as changing appearance/orientation of objects, and changes in the scene. In this paper, we propose a new object classification system for underwater optical images. The proposed method is based on robust feature extraction from fish pattern. A specific preprocessing method is used in order to improve the recognition accuracy. A mean-shift algorithm is charged to segment the images and to isolate objects from background in the raw images. The training data is processed by Principal component analysis (PCA), where we calculate the prior probability inter-features. The decision is given using a combined Bayesian Artificial Neural networks (ANNs). ANNs will calculate non linear relationship of the extracted features, and the posterior probabilities. These probabilities will be verified in the last step in order to keep (or reject) the decision. The comparison of results with state of the art methods shows that the proposed system outperforms most of the solutions in different environmental conditions. The solution simultaneously deals with artificial and reel environment. The results obtained in the simulation indicate that the proposed approach provides a good precision to make distinguish between different fish species. An average accuracy of 94.6% is achieved using the proposed recognition method.