Underwater Cage Boundary Detection Based on GLCM Features by Using SVM Classifier

Underwater vehicle plays an indispensable role in marine observation and biological fishing operations for cage culture. If underwater vehicle could identify the cage boundary autonomously, it can plan the route ahead of time to avoid colliding with the cage, which greatly improves the efficiency of the vehicle and ensure its safety. This paper proposes an automatic Identifying cage boundary technology based on a Gray Level Co-occurrence Matrix by Support Vector Machine Classifier. Based on rich textural features, Gray Level Co-occurrence Matrix (GLCM) was extracted from the cage image and then calculate GLCM features including energy, contrast, entropy, inverse different moment, correlation, and Homogeneity. Support Vector Machine (SVM) classifier is trained with these features and then the classified results were obtained for the query images. The experiments show classification accuracy rate was enough high if there are enough training samples for building training model.