The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 855–861, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-855-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 855–861, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-855-2022
 
30 May 2022
30 May 2022

UAV-BASED RIVER PLASTIC DETECTION WITH A MULTISPECTRAL CAMERA

I. Cortesi1, A. Masiero1, G. Tucci1, and K. Topouzelis2 I. Cortesi et al.
  • 1Dept. of Civil and Environmental Engineering, University of Florence, via di Santa Marta 3, Florence 50139, Italy
  • 2Dep. of Marine Science, University of the Aegean, University Hill, 81100 Mytilene, Greece

Keywords: Plastic, Machine Learning, Random Forest, Connected Regions, Multispectral Camera, Object detection

Abstract. Plastic is the third world’s most produced material by industry (after concrete and steel), but people recycle only 9% of plastic that they have used. The other parts are either burned or accumulated in landfills and in the environment, the latter being the cause of many serious consequences, in particular when considering a long-term scenario. A significant part the plastic waste is dispersed in the aquatic environment, having a dramatic impact on the aquatic flora and fauna. This motivated several works aiming at the development of methodologies and automatic or semi-automatic tools for the plastic pollution detection, in order to enable and facilitate its recovery. This paper deals with the problem of plastic waste automatic detection in the fluvial and aquatic environment. The goal is that of exploiting the well-recognized potential of machine learning tools in object detection applications. A machine learning tool, based on random forest classifiers, has been developed to properly detect plastic objects in multi-spectral imagery collected by an unmanned aerial vehicle (UAV). In the developed approach, the outcome is determined by the combination of two random forest classifiers and of an area-based selection criterion. The approach is tested on 154 images collected by a multi-spectral proximity sensor, namely the MAIA-S2 camera, in a fluvial environment, on the Arno river (Italy), where an artificial controlled scenario was created by introducing plastic samples anchored to the ground. The obtained results are quite satisfactory in terms of object detection accuracy and recall (both higher than 98%), while presenting a remarkably lower performance in terms of precision and quality. The overall performance appears also to be dependent on the UAV flight altitude, being worse at higher altitudes, as expected.