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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video
| Content Provider | MDPI |
|---|---|
| Author | Szymak, Piotr Piskur, Paweł Naus, Krzysztof |
| Copyright Year | 2020 |
| Description | Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment. |
| Starting Page | 3020 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12183020 |
| Journal | Remote Sensing |
| Issue Number | 18 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-16 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Marine Engineering Object Clasiffication in Underwater Video Deep Learning Neural Network Genetic Algorithm Alexnet Densenet 201 Googlenet Inception Resnet V2 Inceptionv3 Mobilenetv2 Nasnet Mobile Resnet 18 Resnet 50 Resnet 101 Shufflenet Squeezenet Vgg 16 Vgg 19 Xception |
| Content Type | Text |
| Resource Type | Article |
