Coral reefs, which are, among the valuable ecosystems on Earth are currently facing unprecedented threats from climate change, pollution and human activities. To address the concerning decline of reefs there is a need for innovative and effective restoration methods. Machine learning algorithms have shown abilities in analyzing ecological data and offering valuable insights to aid decision making processes. In the realm of coral reef restoration, Computer Vision technology can play a role in tackling challenges such as identifying suitable restoration sites efficiently, optimizing deployment strategies and improving monitoring of coral health. Integrating machine learning into efforts to restore reefs shows potential for rehabilitating and conserving these highly endangered ecosystems. By leveraging ML techniques we can gain insights, improve decision making processes, refine restoration strategies and ultimately contribute to the long term resilience and sustainability of reefs. This study delves into the exploration of using YOLO V5 for detecting crown of thorns starfish, on reefs.