Marine debris has a detrimental impact on marine habitat, human health, and the economy. Among various marine debris, abandoned, lost and discarded fishing gear (ALDFG), primarily ghost nets cause the most damage to the environment, fisheries, and shipping. Autonomous underwater vehicles and swarm robotics can be used to clean up and manipulate ghost nets. This requires effective robotic perception, for robots to work in such a challenging environment. In this work, we use binary object detection with transfer learning and the effectiveness of popular YOLOv5 models for real-time detection. Furthermore, we evaluate different monocular depth estimation techniques on ghost nets and couple YOLOv5 with MiDaS for real-time detection and depth estimation.