Abstract
Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral species within the next decade. Restoration efforts are crucial, but their success hinges on introducing automation to upscale efforts. We present automated deployment of coral re-seeding devices powered by artificial intelligence, computer vision, and robotics. Specifically, we perform automated substrate classification, enabling detection of areas of the seafloor suitable for coral growth, thus significantly reducing reliance on human experts and increasing the range and efficiency of restoration. Real-world testing of the algorithms on the Great Barrier Reef leads to deployment accuracy of 77.8%, sub-image patch classification of 89.1%, and real-time model inference at 5.5 frames per second. Further, we present and publicly contribute a large collection of annotated substrate image data to foster future research in this area.
Abstract (translated)
珊瑚礁正处于崩溃的边缘，气候变化、海洋酸化和污染导致预计在未来十年内珊瑚物种将减少70-90%。恢复工作至关重要，但其成功取决于引入自动化以扩大规模。我们提出了一种利用人工智能、计算机视觉和机器人技术自动部署珊瑚重新播种装置的方法。具体而言，我们执行了自动底质分类，能够检测适合珊瑚生长的海底区域，从而大大减少了对人类专家的依赖，并提高了修复工作的范围和效率。在大堡礁进行的实际算法测试中，实现了77.8%的部署准确性、89.1%的小图像块分类准确率以及每秒5.5帧的实时模型推断速度。此外，我们还公开贡献了一大批标注良好的底质图像数据集，以促进该领域的未来研究。
URL
https://arxiv.org/abs/2509.01019
