Deep Learning in Semantic Segmentation of Rust in Images
| Autor: | Muhammad Hassan Khan, Ngo Tung Son, Nguyen Ba Duong, Le Dinh Duy, Nguyen Viet Tung, Ngo Tuan Anh |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Intersection (set theory) Computer science Deep learning Pattern recognition 02 engineering and technology Python (programming language) Convolutional neural network 020204 information systems 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Segmentation Artificial intelligence business computer Encoder computer.programming_language Rust (programming language) |
| Zdroj: | ICSCA |
| DOI: | 10.1145/3384544.3384606 |
| Popis: | Rust detection is an essential topic in many areas, especially in telecommunication, which needs effective systems to segment and recognize rust on power electric towers, antenna. Our exclusive architecture use is based on a fully convolutional neural network for semantic segmentation and composed of Densenet encoder PSP intermediate layers and two skip connections upsample layers. The code written in Python used Pytorch libraries to compute and categorize the images. Comparing between models such as E-Net, U-Net, FCN, we have received our highest FCN (Fully Convolutional Neural) model for the most stable ratio of IoU (Intersection over Union) in 3 models stated with mean scores are 58.1 for origin images and 61.8 for background removal. With the results, we will contribute to detect rust on electric poles in time to avoid rust-causing serious consequences. |
| Databáze: | OpenAIRE |
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