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Moreover, visual techniques can be easily combined with the traditional use of magnetometers and scanning imaging sonars, to improve the effectiveness of the survey. The proposed system can be easily adapted to other scenarios (e.g., underwater archeology or visual inspection of underwater pipelines and implants), by simply replacing the Convolutional Neural Network devoted to the visual identification task. As a final outcome of our work we provide a large dataset of images of explosive materials: it can be used to compare different visual techniques on a common basis.<\/jats:p>","DOI":"10.3233\/ica-220675","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T13:21:45Z","timestamp":1645536105000},"page":"123-139","source":"Crossref","is-referenced-by-count":13,"title":["An integrated low-cost system for object detection in underwater environments"],"prefix":"10.1177","volume":"29","author":[{"given":"Gian Luca","family":"Foresti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Scagnetto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/ICA-220675_ref1","doi-asserted-by":"crossref","unstructured":"Ancuti C, Ancuti CO, Haber T, Bekaert P. Enhancing underwater images and videos by fusion. 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