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...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.展开更多
文摘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.
