Real-time and embedded compact deep neural networks for seagrass monitoring

— We propose an efficient and robust segmentation network for automated seagrass region detection. The proposed network has a simple architecture to save computational demands as well as inference energy cost. More importantly, the scale of network can be feasibly adjusted, to balance the network computational demands and segmentation accuracy. Experimental results show that our proposed network is robust to segment the various seagrass patterns with 90.66% mIoU (mean Intersection over Union) accuracy. It had achieved 200 frames per second (FPS, 1.42 times faster than the second-best network GCN) on desktop GPU, and 18 FPS on NVIDIA Jetson TX2. It also has 3.45M parameters and 0.587 GMACs FLOPs (FLoating Point OPerations), only 14.6% and 10.8% of those in GCN respectively. To segment a single image on the Jetson TX2, our architecture requires an average energy of 0.26 Joule. This energy cost is only 46% of DeepLab, which shows the proposed network to be an energy efficient one. The proposed network demonstrates accurate and real-time segmentation capability, and it can be deployed to low-energy embedded AUVs for sea habitat protection.