# Deep-Feature-Based Approach to Marine Debris Classification
## 4 Results
### Deep CNN Architectures
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Recall (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} & \multirow{2}{*}{**Kappa**} \\  & & & **Macro** & & **Weighted** & **Macro** & & **Weighted** \\ \hline \multirow{3}{*}{**VGG19**} & FFE & 77.15 & 80.22 & 77.87 & 77.23 & 78.15 & 76.87 & 0.71 \\  & FT & 85.74 & 85.79 & 85.89 & 83.94 & 84.62 & 85.64 & 0.82 \\  & FFE + FT & **86.16** & **86.55** & **86.16** & **85.80** & **86.15** & **86.13** & **0.83** \\ \hline \multirow{3}{*}{**InceptionV3**} & FFE & 81.34 & 82.62 & 82.14 & 81.45 & 81.75 & 81.46 & 0.77 \\  & FT & **90.57** & 90.82 & **90.54** & **91.39** & **91.07** & **90.53** & **0.88** \\  & FFE + FT & 89.94 & **91.14** & 90.12 & 89.46 & 90.13 & 89.91 & 0.87 \\ \hline \multirow{3}{*}{**ResNet50**} & FFE & 79.04 & 82.15 & 79.68 & 79.07 & 80.15 & 79.02 & 0.74 \\  & FT & 85.12 & **86.60** & 85.34 & 83.51 & 84.65 & 85.02 & 0.81 \\  & FFE + FT & **85.53** & 86.31 & **85.59** & **86.82** & **86.51** & **85.51** & **0.82** \\ \hline \multirow{3}{*}{**Inception-ResNetV2**} & FFE & 81.97 & 84.63 & 82.44 & 81.02 & 82.54 & 81.99 & 0.77 \\  & FT & **91.40** & 91.91 & **91.40** & **92.32** & **92.08** & **91.38** & **0.89** \\  & FFE + FT & 90.78 & **91.97** & 90.81 & 91.20 & 91.46 & 90.66 & 0.88 \\ \hline \multirow{3}{*}{**DenseNet121**} & FFE & 83.02 & 85.07 & 83.72 & 83.81 & 84.19 & 83.14 & 0.79 \\  & FT & **88.05** & **89.19** & **88.29** & **87.61** & **88.26** & **88.03** & **0.85** \\  & FFE + FT & 87.21 & 87.90 & 87.38 & 86.54 & 87.05 & 87.16 & 0.84 \\ \hline \multirow{3}{*}{**MobileNetV2**} & FFE & 79.04 & 79.83 & 78.99 & 77.91 & 78.56 & 78.76 & 0.74 \\  & FT & **82.60** & **83.57** & **82.65** & **82.13** & **82.61** & **82.49** & **0.78** \\ \cline{1-1}  & FFE + FT & 81.76 & 81.55 & 82.32 & 79.84 & 80.52 & 81.87 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of NN classifiers on the test set. Since the value of the weighted recall is equal to the value of accuracy, we present only macro recall values. these classes. Greater separation between semantic clusters of features can be seen in the better-performing FT Inception model (Figure 17). In general, models obtain higher precision than recall, i.e., fewer false positives than false negatives per class. Figure 18 shows class-wise F1-scores for each deep CNN architecture. The weakest performance is observed on the _Other garbage_ class with high in-class variations and the best in the _Rubber_ class due to the unique round shape of class objects and lower range of in-class variations. Aside from the low performance in the _Other Trash_ class, models that used the ResNet50 and VGG19 extractors did not show the best performance in the _Glass_ class either. Figure 19 shows predictions of the FT Inception model on new images of waste items collected by local divers in the Croatian Adriatic sea, more precisely, in the bays in the area Split and the island of Korcula. These local images were not used during the training nor testing. They are the first step toward creating a new local dataset for marine debris  
Figure 17: Two-dimensional t-SNE projections of Inception-ResNetV2 and MobileNetV2 features. Figure 18: Class-wise F1-scores of FT NN classifiers. classification and identification in Adriatic Sea underwaters. The network trained on deep-sea images shows promising results in a new aquatic environment with different sea transparency and salinity, seabed sediments, and unique flora and fauna.