DeepRecog: Threefold underwater image deblurring and object recognition framework for AUV vision systems.

Underwater explorations and probes have now become frequent for marine discovery and endangered resources protection. The decrease in natural light with an increase in water depth and the characteristic of the medium to absorb and scatter light pose crucial challenges to underwater vision systems. Autonomous Underwater Vehicles (AUVs) depend upon their imaging systems for navigation and environmental resource exploration. This paper proposes DeepRecog—an integrated underwater image deblurring and object recognition framework for AUV vision systems. The principle behind the image deblurring module involves a threefold approach consisting of CNNs, adaptive and transformative filters. The ensemble object detection and recognition module identifies marine life and other frequently existent underwater assets from AUV images and achieves mean Average Precision (mAP) of 0.95 and was found to be 6.42% more precise than YOLOv3, 8.43% more than FasterRCNN + VGG16 and 15.78% more than FasterRCNN. This framework was created with the purpose of providing real-time detection and recognition with minimal delay. The system can also be employed for former images acquired from AUVs and hopes to facilitate efficient solutions for marine image post-processing. [ABSTRACT FROM AUTHOR]