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AI-Enabled Electronic Monitoring Solutions (view source)
Revision as of 10:58, 7 December 2023
, 10:58, 7 December 2023no edit summary
The AI-assisted solution aligns with the business goal of enhancing the quality of data collected through the electronic monitoring program by minimizing human error in catch apportionment and lessening the impact of catch apportionment errors on catch estimates (weight). Furthermore, this solution is in sync with the aim of improving the efficiency of the review process, ensuring timely access to catch information. It generates the potential for accurate catch estimates (weight) on every tow in every trip, maintaining the same reviewer time investment, which increases review efficiency and effectiveness. Additionally, it provides the potential for summary data at the vessel, fishery, and regional levels, thereby aiding compliance with total catch quotas.
The AI-assisted solution aligns with the business goal of enhancing the quality of data collected through the electronic monitoring program by minimizing human error in catch apportionment and lessening the impact of catch apportionment errors on catch estimates (weight). Furthermore, this solution is in sync with the aim of improving the efficiency of the review process, ensuring timely access to catch information. It generates the potential for accurate catch estimates (weight) on every tow in every trip, maintaining the same reviewer time investment, which increases review efficiency and effectiveness. Additionally, it provides the potential for summary data at the vessel, fishery, and regional levels, thereby aiding compliance with total catch quotas.
== Computer Vision Results ==
== Model Development and Performance Results ==
== Demo ==
== Demo ==
Click [https://vimeo.com/892077942 here] for a demo of the tool.
Click [https://vimeo.com/892077942 here] for a demo of the tool.
== The Proof of Value Phase ==
The project currently is at the proof of value phase. The process for reviewing involves reviewers following their usual routine, where clips relevant to the participating boats from different tows are extracted from the loaded footage into FishVue Interpret and then loaded into the Proof of Concept solution. The reviewer is tasked with selecting at least 3 to 5 images from each tow for catch apportionment analysis. Afterward, they will review these catch apportionment results, utilizing them to inform and make more accurate catch estimates. Moreover, a sample of tows will undergo a full manual review. This step is crucial as it allows for the assessment of the impact on the accuracy of the catch estimates.
* The Key Performance Indicators (KPIs) for the Proof of Value phase include the following:
* Number of catch apportionment results rejected by reviewers (single images)
* Number of total catch apportionment results rejected by reviewers (tows)
* Accuracy of catch apportionment (deviation compared with manual review)
* Delta in results between first and second iterations
* Change in administrative user feedback between iterations if any UI/UX adjustments are made
== Next Steps ==
== Next Steps ==
Following the success of both the proof of concept and the proof of value, DFO plans to promote the AI-assisted solution for Pacific trawl fisheries into production in the fiscal years 2023-2024. Furthermore, the department is planning another pilot project for the use of AI in Snow Crab fisheries in the Quebec region.
Following the success of both the proof of concept and the proof of value, DFO plans to promote the AI-assisted solution for Pacific trawl fisheries into production in the fiscal years 2023-2024. Furthermore, the department is planning another pilot project for the use of AI in Snow Crab fisheries in the Quebec region.