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.