Enhancement and Detection of Objects in Underwater Images using Image Super-resolution and Effective Object Detection Model

It is imperative to build an automatic underwater object recognition system in place to reduce the costs of underwater inspections as well as the associated risks. An effective method of detecting underwater objects from underwater images of aquatic after enhancing them using the Image Super-resolution technique is proposed in this study. The proposed approach comprises of two major sections, Underwater Image Enhancement, and Object detection. To enhance the underwater images, a lightweight Reduced Cascading Residual Network (RCARN) is proposed that imposes the Image Super-resolution technique. Later, the enhanced images generated by the RCARN model are supplied for the object detection process, where a significant object detection model, YOLOv3 is employed in this study. To improve its performance, this YOLOv3 is trained on one of the largest datasets, the COCO data, followed by being fine-tuned using enhanced Underwater images. The dataset utilized in this work contains 6 classes of underwater objects namely dolphin, jellyfish, octopus, seahorse, starfish, and turtle. All these images are actual real field images collected from various sources. With this proposed approach, a better overall ACS and mAP of 95.44% and 75.33% are achieved here, which are improved by ~8.75% and ~15%, respectively when compared to actual collected low-resolution images

Enhancement and Detection of Objects in Underwater Images using Image Super-resolution and Effective Object Detection Model

1050-1060It is imperative to build an automatic underwater object recognition system in place to reduce the costs of underwater inspections as well as the associated risks. An effective method of detecting underwater objects from underwater images of aquatic after enhancing them using the Image Super-resolution technique is proposed in this study. The proposed approach comprises of two major sections, Underwater Image Enhancement, and Object detection. To enhance the underwater images, a lightweight Reduced Cascading Residual Network (RCARN) is proposed that imposes the Image Super-resolution technique. Later, the enhanced images generated by the RCARN model are supplied for the object detection process, where a significant object detection model, YOLOv3 is employed in this study. To improve its performance, this YOLOv3 is trained on one of the largest datasets, the COCO data, followed by being fine-tuned using enhanced Underwater images. The dataset utilized in this work contains 6 classes of underwater objects namely dolphin, jellyfish, octopus, seahorse, starfish, and turtle. All these images are actual real field images collected from various sources. With this proposed approach, a better overall ACS and mAP of 95.44% and 75.33% are achieved here, which are improved by ~8.75% and ~15%, respectively when compared to actual collected low-resolution images