Abstract
In recent years, the large amount of debris scattered throughout the ocean is becoming one of the major pollution problems, causing extinction of species and accelerating the degradation of our planet, among other environmental issues. Since the manual treatment of this waste represents a considerably tedious task, autonomous frameworks are gaining attention. Due to their reported good performance, such frameworks generally rely on Deep Learning techniques. However, the scarcity of data coupled with the inherent difficulties of the field—debris with different shapes and colors due to long-lasting exposure to the ocean, illumination variability or sea conditions—makes detecting underwater objects a particularly challenging task. The contribution of this work to the field is double: on the one hand, we introduce a novel data collection for supervised learning—the CleanSea corpus—annotated at both the bound box and contour levels of the objects to contribute with the research and progress in the field; on the other hand, we devise and optimize a recognition model based on the reference Mask Object-Based Convolutional Neural Network for this set to establish a benchmark for future comparison and assess its performance in both simulated and real-world scenarios. Results show the relevance of the contributions as the devised model is capable of properly addressing the detection and recognition of general debris when trained with the introduced CleanSea corpus.
Work supported by the Pattern Recognition and Artificial Intelligence Group (PRAIg) from the University of Alicante, Spain. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”.
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Notes
- 1.
For conciseness, we only report the subset of the results in which the highest variation was obtained.
- 2.
Note that the debris in the videos may not necessarily match that of the corpus.
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Sánchez-Ferrer, A., Gallego, A.J., Valero-Mas, J.J., Calvo-Zaragoza, J. (2022). The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_49
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