# Deep-Feature-Based Approach to Marine Debris Classification
## 5 Discussion  
This study analyzes the performance of well-known deep CNN architectures when utilized as feature extractors to classify underwater images of marine debris with neural networks and other conventional ML classifiers. Fine-tuned deep CNN feature extractors that stack Inception modules, Inception-ResNetV2 and InceptionV3, show the overall best performance in our experimental setup (see Tables 5 and 6). With increasing depth, these architectures simultaneously increase the width of the network by introducing parallel convolutions. Although MobileNetV2 architecture's performance is ranked as the worst, it should not be written off easily since this architecture comes with several advantages desirable for implementation in embedded devices: faster performance, reduced network size, and low latency. With the choice of MobileNetV2 architecture for feature extraction, one sacrifices overall accuracy for computational efficiency. Several works have employed deep-learning-based approaches to marine debris detection and classification in underwater imagery in recent years. Table 7 gives an overview of results presented in the literature using different network architectures. While [27; 28; 29] use realistic underwater images, [23] uses forward-looking sonar (FLS) imagery with constructed 96 \(\times\) 96 image crops of debris objects utilized for classifier's training and testing. In terms of accuracy, classification model from [23] gives better results than similar models trained and validated on underwater RGB images that do not contain only one centered debris object, as expected. In contrast to studies presented in Table 7, our work compares the performance of six well-known CNN architectures (VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNetV2, and MobileNetV2) combined with NN, RF, SVM, NB,  
Figure 20: Normalized confusion matrices. Confusion matrices in the first row show predictions of NN classifiers with FT deep feature extractors, while those in the second row show predictions of the conventional ML classifier with the best performance on the test set. LR, and KNN classifiers. The best reported result with an accuracy of 91.61% is obtained with fine-tuned Inception-ResNetV2 feature extractor and SVM classifier. Despite the intrinsic challenges of the used dataset, with the choice of appropriate feature-extractor network architectures, the scheme for its deployment, and the appropriate classifier, satisfactory results on new images are obtained. This paper focuses exclusively on image classification based on deep learning feature extraction. Alternatively, traditional computer vision techniques can be used for feature extraction from raw pixel data. The problem with traditional approach is that it requires careful fine-tuning and expert analysis. This is especially evident in multiclass classification problems. Each class requires manual feature engineering in order to describe best its typical object patterns, which becomes a real burden with many parameters to tweak [49]. On the other hand, DL methods mitigate the need for manual extraction of features and provide the end-to-end learning process, which extracts relevant image features automatically and often outperforms conventional feature extraction techniques [73; 74]. Traditional \"crisp\" algorithms for image classification do not fully consider inherent uncertainties and peculiarities present in debris images from the realistic underwater environment. Image entities belonging to the same debris class often significantly vary in shape and entity position. Moreover, images are often affected by uncertainties such as current recording angle, water turbidity, and illumination, implying the inherent fuzzy nature of the given dataset. In order to address this problem, specific classifiers based on fuzzy techniques can be applied. The success of the fuzzy approach to classification and clustering has been reported on datasets with inherent uncertainties from various domains [75; 76; 77].