Comparison of Feature Extraction Methods and Deep Learning Framework for Depth Map Recognition

Sýkora, Peter; Kameneay, Patrik; Hudec, Róbert; Benčo, Miroslav; Šinko, Martin

doi:10.23919/ntsp.2018.8524109

Cited by 4 publications

(1 citation statement)

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“…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].…”

Section: Discussionmentioning

confidence: 99%

Deep-Feature-Based Approach to Marine Debris Classification

2021

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The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of 91.40% and F1-score 92.08%, followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers’ performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data.

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Section: Discussionmentioning

confidence: 99%