Pixelwise annotation of coral reef substrates

Coral reef substrate composition is regularly surveyed for ecosystem health monitoring. The current method of visual assessment is slow and limited in scale. ImageCLEFcoral aims to identify reef areas of interest and annotate them appropriately. We present an adaptation of a submission to the 2019 ImageCLEFcoral task that uses a semantic segmentation model, DeepLabV3, with a ResNet-101 backbone. We implemented pre-training image colour enhancement and supplemented the available training data with that of NOAA NCEI for specific runs. Our runs had no overall improvement from the 2019 code, though did predict submassive corals and table corals with greater accuracy (+3.022% and +0.353%). Though none of our model runs had the highest precision or accuracy, we did best predict submassive corals (3.022%), boulder corals (12.787%), table corals (0.353%), foliose corals (0.097%), gorgonian soft corals (0.002%) and algae (0.027%) across 3 of our 4 runs. Image colour enhancement benefited the prediction accuracy of boulder corals (+1.209−5.026%), encrusting corals (+1.7−2.578%) and algae (+0.027%), most likely by making them more distinct from their surroundings. Adding NOAA data enhanced the precision of encrusting coral, soft coral and gorgonian predictions despite only providing additional annotations for encrusting and foliose corals. Our results suggest that a more balanced approach to data augmentation combined with image-specific colour improvements may provide a more desirable outcome, particularly when paired with a model that is fine-tuned to the data set used.