Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging
This paper presents a pioneering study in the application of real-time surface landmine detection using a combination of robotics and deep learning. We introduce a novel system integrated within a demining robot, capable of detecting landmines in real time with high recall. Utilizing YOLOv8 models,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/4/677 |
| _version_ | 1827342828326354944 |
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| author | Emanuele Vivoli Marco Bertini Lorenzo Capineri |
| author_facet | Emanuele Vivoli Marco Bertini Lorenzo Capineri |
| author_sort | Emanuele Vivoli |
| collection | DOAJ |
| description | This paper presents a pioneering study in the application of real-time surface landmine detection using a combination of robotics and deep learning. We introduce a novel system integrated within a demining robot, capable of detecting landmines in real time with high recall. Utilizing YOLOv8 models, we leverage both optical imaging and artificial intelligence to identify two common types of surface landmines: PFM-1 (butterfly) and PMA-2 (starfish with tripwire). Our system runs at 2 FPS on a mobile device missing at most 1.6% of targets. It demonstrates significant advancements in operational speed and autonomy, surpassing conventional methods while being compatible with other approaches like UAV. In addition to the proposed system, we release two datasets with remarkable differences in landmine and background colors, built to train and test the model performances. |
| first_indexed | 2024-03-07T22:16:09Z |
| format | Article |
| id | doaj.art-9d907b2ae91b4b13a00d749231b19e42 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-07T22:16:09Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9d907b2ae91b4b13a00d749231b19e422024-02-23T15:33:03ZengMDPI AGRemote Sensing2072-42922024-02-0116467710.3390/rs16040677Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical ImagingEmanuele Vivoli0Marco Bertini1Lorenzo Capineri2Media Integration and Communication Center (MICC), Department Information Engineering, University of Florence, Viale Giovanni Battista Morgagni, 65, 50134 Florence, ItalyMedia Integration and Communication Center (MICC), Department Information Engineering, University of Florence, Viale Giovanni Battista Morgagni, 65, 50134 Florence, ItalyUltrasound and Non-Destructive Testing Laboratory (USCND), Department Information Engineering, University of Florence, Via di Santa Marta, 3, 50139 Florence, ItalyThis paper presents a pioneering study in the application of real-time surface landmine detection using a combination of robotics and deep learning. We introduce a novel system integrated within a demining robot, capable of detecting landmines in real time with high recall. Utilizing YOLOv8 models, we leverage both optical imaging and artificial intelligence to identify two common types of surface landmines: PFM-1 (butterfly) and PMA-2 (starfish with tripwire). Our system runs at 2 FPS on a mobile device missing at most 1.6% of targets. It demonstrates significant advancements in operational speed and autonomy, surpassing conventional methods while being compatible with other approaches like UAV. In addition to the proposed system, we release two datasets with remarkable differences in landmine and background colors, built to train and test the model performances.https://www.mdpi.com/2072-4292/16/4/677deep learningartificial intelligenceoptoelectronic sensorslandmineUXOdetection |
| spellingShingle | Emanuele Vivoli Marco Bertini Lorenzo Capineri Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging Remote Sensing deep learning artificial intelligence optoelectronic sensors landmine UXO detection |
| title | Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging |
| title_full | Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging |
| title_fullStr | Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging |
| title_full_unstemmed | Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging |
| title_short | Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging |
| title_sort | deep learning based real time detection of surface landmines using optical imaging |
| topic | deep learning artificial intelligence optoelectronic sensors landmine UXO detection |
| url | https://www.mdpi.com/2072-4292/16/4/677 |
| work_keys_str_mv | AT emanuelevivoli deeplearningbasedrealtimedetectionofsurfacelandminesusingopticalimaging AT marcobertini deeplearningbasedrealtimedetectionofsurfacelandminesusingopticalimaging AT lorenzocapineri deeplearningbasedrealtimedetectionofsurfacelandminesusingopticalimaging |