Faster R-CNN Based Deep Learning for Seagrass Detection from Underwater Digital Images

Deep learning-based techniques have gained unprecedented success for object detection tasks. The state of the art object detection accuracy and robustness have been achieved by Faster R-CNN framework based algorithms. However, no attempts have been made to detect seagrasses from underwater images mostly due to lack of labelled ground truth dataset, and additional challenges imposed by underwater photographs and low boundary differences among the seagrass and surrounding vegetation. We have created a dataset consisting of 2,699 underwater images of Halophila ovalis (one of the common type of seagrasses from Indo-Pacific saltwater environments [1]). We have labelled the seagrass and implemented Faster R-CNN based object detector to detect them from underwater images. We have used Inception V2 network in the Faster R-CNN pipeline and found, this network showed a high mean average precision (mAP) of 0.3464 on laboratory images only, and 0.261 on a test set consists of both field and laboratory images.