Machine Learning for detecting sharks

The identification of animals and objects in complex environments is an area of computer vision which has largely been neglected due to high expense of datasets and intensive computational requirements for analysis.  The challenges of identifying sharks and aquatic animals in environments littered with surfers, rocks and various other possible false positives required the development of a system incorporating the newest forms of computer vision machine learning. Simple methods such as color separation, intensity histograms and optical flow are all thrown off by the ever changing complex environment of the ocean.  For this reason a machine learning method that gradually improves with larger and larger datasets was the best solution.

The development of this system needs to overcome a few large obstacles that are present in this dynamic environment

  • Ability to identify sharks from a minimum 60 meters altitude from a fast moving drone
  • Detection through the highly changing environment of waves, glare and shadows created from the changing ocean
  • False positives from surfers and a range of other marine life
  • Ability to be run on an onboard single board computer
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Examples of difficult environments

With use of machine learning methods the ability to essentially train out specific problematic areas and accentuate features of the shark for easier detection become feasible.  This has required the use of immensely huge datasets with over 50,000 individual images being utilized in order to successfully differentiate between the aggressive ocean and the targeted entity.  The result has been extremely successful over other techniques with low false positives and high identification in extremely complicated environments. The problems of waves, shadows and rocks have all been slowly trained out to a point where blind runs of unseen datasets produce high identification rates and low false positives.

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The capabilities of this machine learning technique are near endless with the addition of more computational power the ability to run identification of multiple animals and objects can be undertaken. This can lead to a huge new range of possibilities in creating specific scenarios for warning systems and data collection.

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Do you have a similar project in mind? Want to successfully integrate remotely piloted aircraft into your operation?  We would love to help you turn your idea into practical results. Contact us today. 

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