Seagrass detection from underwater digital images using faster R-CNN with NASNet
2021 Digital Image Computing: Techniques and Applications (DICTA)
School of Science
In recent years, it has been demonstrated that deep learning has great success in a variety of computer vision applications. Deep learning-based Faster R-CNN algorithm depends on region proposal network that provides state-of-the-art object detection performance. To date, a limited number of Faster R-CNN approaches have been attempted to detect seagrass from underwater digital images. This paper proposes an improved seagrass detector that enhances the detection performance by combining the Faster R-CNN framework with the NASNet-A backbone. This seagrass detector achieves a high mean average precision (mAP) of 0.412 on ECUHO-2 dataset, which is significantly better than state-of-the-art Halophila ovalis detection performance on this dataset.