Defining materials using laser signals from long distance via deep learning
Material identification is useful in robotics, industrial manufacturing, autonomous driving and so on. Cameras are generally used in material identification studies. However, in cases where lighting conditions are not suitable, material detection with cameras is difficult. In the proposed system, th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447921003683 |
| _version_ | 1811294145048739840 |
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| author | Nevzat Olgun İbrahim Türkoğlu |
| author_facet | Nevzat Olgun İbrahim Türkoğlu |
| author_sort | Nevzat Olgun |
| collection | DOAJ |
| description | Material identification is useful in robotics, industrial manufacturing, autonomous driving and so on. Cameras are generally used in material identification studies. However, in cases where lighting conditions are not suitable, material detection with cameras is difficult. In the proposed system, the defining of the target material, is realized with the LSTM deep learning model using only one laser light source that works independently of the environment light. Objects located at a certain distance are marked with a low-powered laser light source and laser signals reflected from the objects are recorded with the sensor system. After the data preparation steps, laser signals are trained with the LSTM deep learning model and the classification process is performed. For this purpose, laser signals recorded from 10 different materials at a certain distance, which are frequently used in daily life, are accurately classified with an average of 93.63% with the LSTM model. Experimental studies show that material defining can be performed using the LSTM deep learning model from a single laser measurement point. |
| first_indexed | 2024-04-13T05:13:07Z |
| format | Article |
| id | doaj.art-9bbbbbb5971242ab97dcba2c12f1f4e5 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-13T05:13:07Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-9bbbbbb5971242ab97dcba2c12f1f4e52022-12-22T03:00:59ZengElsevierAin Shams Engineering Journal2090-44792022-05-01133101603Defining materials using laser signals from long distance via deep learningNevzat Olgun0İbrahim Türkoğlu1Zonguldak Bulent Ecevit University/Department of Computer Technologies, Zonguldak, Turkey; Corresponding author.Firat University/Department of Software Engineering, Elazig, TurkeyMaterial identification is useful in robotics, industrial manufacturing, autonomous driving and so on. Cameras are generally used in material identification studies. However, in cases where lighting conditions are not suitable, material detection with cameras is difficult. In the proposed system, the defining of the target material, is realized with the LSTM deep learning model using only one laser light source that works independently of the environment light. Objects located at a certain distance are marked with a low-powered laser light source and laser signals reflected from the objects are recorded with the sensor system. After the data preparation steps, laser signals are trained with the LSTM deep learning model and the classification process is performed. For this purpose, laser signals recorded from 10 different materials at a certain distance, which are frequently used in daily life, are accurately classified with an average of 93.63% with the LSTM model. Experimental studies show that material defining can be performed using the LSTM deep learning model from a single laser measurement point.http://www.sciencedirect.com/science/article/pii/S2090447921003683LaserLSTMMaterial detectionSignal processing |
| spellingShingle | Nevzat Olgun İbrahim Türkoğlu Defining materials using laser signals from long distance via deep learning Ain Shams Engineering Journal Laser LSTM Material detection Signal processing |
| title | Defining materials using laser signals from long distance via deep learning |
| title_full | Defining materials using laser signals from long distance via deep learning |
| title_fullStr | Defining materials using laser signals from long distance via deep learning |
| title_full_unstemmed | Defining materials using laser signals from long distance via deep learning |
| title_short | Defining materials using laser signals from long distance via deep learning |
| title_sort | defining materials using laser signals from long distance via deep learning |
| topic | Laser LSTM Material detection Signal processing |
| url | http://www.sciencedirect.com/science/article/pii/S2090447921003683 |
| work_keys_str_mv | AT nevzatolgun definingmaterialsusinglasersignalsfromlongdistanceviadeeplearning AT ibrahimturkoglu definingmaterialsusinglasersignalsfromlongdistanceviadeeplearning |