Artificial-Intelligence-Based Low-Light Marine Image Enhancement for Semantic Segmentation in Edge-Intelligence-Empowered Internet of Things Environment

For accurate detection of marine life to utilize marine resources while ensuring protection of ecosystem, marine animal segmentation (MAS) has been widely researched. Furthermore, development of autonomous underwater vehicle (AUV) has expanded the scope of marine ecosystem research into deep sea where AUV utilizes artificial light sources to address the problem of low-light conditions. However, these light sources can disturb the ecosystem. In addition, extremely low-light images are acquired in areas distant from AUV due to the limitations of the light sources, such as limited field of view, resulting in poor quality of underwater images. Therefore, we propose multiscale features and residual dual attention-based low-light image enhancement network (MRLE-Net) for semantic segmentation of marine images. To preserve fine-grained information under low-light environment and reduce noise, MRLE-Net introduces dual feature extraction, multiscale feature extraction, and residual dual attention blocks. Furthermore, to improve the semantic segmentation accuracy, it employs a discrete wavelet transform-based loss function. In experiments using two open databases of MAS3K and DeepFish, the mean intersection of union values of semantic segmentation by our method are 78.72% and 83.62%, respectively, showing superior accuracy to the state-of-the-art methods. In addition, our MRLE-Net demonstrates its ability to operate on embedded system with low-computational resources as edge computing. From them, we confirm that it can be adopted to AUV in edge intelligence empowered Internet of Things environment by removing communication overheads caused by transmitting lots of images from AUV's camera to and receiving the segmentation result from high-computing cloud by 5G technology.