Aerial Surveillance for Coast Safety to Shark Detection using Image Identification

Teiki Claveau, Chin E. Lin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Shark detection in uncontrolled environment is a challenging problem that has not been paid much attention deeply. How to accomplish fast and effective coast surveillance impacts safety concerns in beach activities. This paper proposes a submerged shark detection, such as the white shark, using image identification from low altitude drones. The image identification is set on a drone to train real datasets of a 2.5 m long and 0.945 m2 shark model. The Haar feature-based cascade classifier is used to detect regions of interest (ROI) to extract some features to classify water area with a shark. The proposed system is tested in two different uncontrolled areas in Kenting coast, Taiwan by presenting contrasted conditions. The adopt technique reaches an average of 19 frames/second based on different altitudes of drone experiments from 8 to 22 m above sea level in static and dynamic detections for 30 minutes endurance. The system achieves a true detection's average of 99.5% by correct classification and the mean score on total false positive detection is 3.85%. The detection rate varies with the altitude and the weather conditions which is sensitive in building an altitude-based image detection system. The experiments show very effective results to detect sharks on sea coast to reach a lower false positive detection rate.

Original languageEnglish
Pages (from-to)251-266
Number of pages16
JournalJournal of Aeronautics, Astronautics and Aviation
Volume49
Issue number3
DOIs
Publication statusPublished - 2017 Sep 1

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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