Hybrid Color Space and GLCM Feature Extraction Based Classification Method for Underwater Image Analysis

Abstract

In cases where man-made fishing nets are forgotten or lost underwater, these nets are called ghost nets. These ghost nets threaten the underwater ecosystem over time and reduce the biodiversity of living creatures underwater. For this reason, studies are being carried out to detect ghost nets underwater. In this study, SODD and Trash Icra data sets were used to detect net, trash, ROV and biological creature classes underwater. For each class, features were extracted using the GLCM filter and HSV, YUV, LAB and RGB color spaces. The extracted features were trained with the Random Forest Classification Algorithm and the results were obtained. As a result of the training, it was seen that although each color space had a low accuracy value on its own, when used together, it affected the performance positively and increased the accuracy, and the best accuracy value was 89.16% in the proposed method (HSV + YUV + LAB + RGB + GLCM). In addition, for the best case where all color spaces were used, Naive Bayes(NB), KNN and SVM classification algorithms were applied and the results were compared with the proposed method, Random Forest Classification. Accuracy values of 52% with NB, 62.17% with KNN and 73.67% with SVM were obtained and it was proven that the best method was the proposed method, Random Forest Classification algorithm. The results of the study demonstrate the effectiveness of integrating multi-color space features with texture analysis for underwater object classification, offering a promising approach for ghost net detection in real-world scenarios. Ghost nets, which are abandoned or lost man-made fishing nets, pose a significant threat to the marine ecosystem by entangling and endangering underwater life.