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Advising Organization:Ocean Affairs Council
As marine debris becomes a critical global issue, leveraging AI technology to enhance environmental monitoring is a major international trend. To accelerate the digital transformation of marine governance, this "International AI Challenge for Marine Debris Image Recognition" will release a high-quality marine debris image dataset. We invite outstanding talents from global academia, research institutions, industry, and tech communities to develop high-precision object detection models. Your participation will drive innovation in marine monitoring and promote practical AI applications in environmental governance.
This competition is a vital platform connecting global innovative energy. Whether you have a background in AI, data analysis, computer vision, marine science, or are simply passionate about sustainability, you are welcome to join. Teams will not only have the opportunity to win prize money and gain international exposure but will also use real-world data to transform technical innovation into tangible environmental impact. Join us to respond to marine sustainability challenges with AI and build a smarter future together!
Teams ranked in the top 10 on the Private Leaderboard with an mAP@0.5 score of 0.6 or above will qualify for the Final Round. The Final Round offers a total prize pool of up to NT$300,000.
All event times are based on Taiwan Time (GMT+8).
| Date | Event |
|---|---|
| 2026/07/08 | Registration Opens |
| 2026/07/17 17:00~18:00 | Online Information Session |
| 2026/07/19 18:00~19:00 | Online Information Session |
| 2026/08/10 23:59:59 | Registration Deadline |
| 2026/08/15 | Preliminary Technical Workshop |
| 2026/08/17 10:00:00 | Test Set Released & Available for Download |
| 2026/08/21 10:00:00 | Submission for Preliminary Answers |
| 2026/09/10 23:59:59 | Deadline for Preliminary Submissions |
| 2026/09/14 | Announcement of Finalists |
The evaluation metric uses mean Average Precision (mAP)[1] at an Intersection over Union (IoU)[2] threshold of 0.5. A prediction bounding box is considered a True Positive (TP) if its IoU with the ground truth bounding box is greater than 0.5; otherwise, it is a False Positive (FP), from which precision is derived. The system evaluates the AP score for each object type and then averages the AP values across the 19+1 classes of marine debris objects to obtain the final mAP evaluation value, which determines the ranking.
The system uses the COCO API[3] to calculate the mAP values.
Reference
[1] Average Precision (AP): Average precision
[2] intersection over union (IoU): Jaccard index
[3] COCO API:
https://github.com/cocodataset/cocoapi