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Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D. et al. (2015). Microsoft COCO: Common Objects in Context. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision—ECCV 2014 (pp. 740-755). Springer International Publishing.
https://doi.org/10.1007/978-3-319-10602-1_48

has been cited by the following article:

• TITLE: Beach Surveillance: A Contribution to Automation

  AUTHORS: Maria da Conceição Proença, Ricardo Nogueira Mendes

  KEYWORDS: People Counting, Beach Surcharge, Human Detectors, Deep Learning Methodologies

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.12 No.12, December 25, 2024

  ABSTRACT: The problem of human overload in many habitats is becoming increasingly urgent, as it is the driving force that destroys ecosystems beyond repair. This paper describes a possible workflow for beach surveillance, using a deep learning solution available online that runs on a standard laptop with RGB images acquired with a standard camera. The software is YOLO v7, a state-of-the-art real-time object detection model presently used for autonomous driving, surveillance, and robotics. The workflow and parametrization needed for building a model are described, along with examples of the results over 180 test images that ensures an overall precision of 0.98 and recall of 0.94 (F1 = 0.96). The model was parametrized to focus on a minimum number of false positives; from the 5672 possible detections identified by human curation, 5285 were correctly identified and located, 387 missed and there are 116 mistakes. A minimum of computational skills is needed to reproduce this implementation in any user data of the same kind.