The purpose of this study is to investigate the application of deep neural network for the Automated Underwater Vehicle in the subsea pipeline inspection. In today’s modern world, we see more and more sophisticated and precise computer vision object detection technology being implemented in our daily lives. To name a few, security cameras, self-driving cars, drones and more. This research suggests that computer vision pipeline defect detection is an attractive solution for the future subsea pipeline defect detection as it relies less human intervention and is more reliable. In this paper, review of the current methods of subsea pipeline inspection was done. Some of the methods with visual detection and their limitations are also discussed. Apart from that, the machine learning algorithm of our focus, Faster RCNN is studied. Beyond that, we have explained the methods of our experimentation and the process of training as well as validating the custom dataset. The outcomes are separated into different sections, training curve, visual data representation as well as the model accuracy in different operating environment. In conclusion, we found that the model is able to detect the underwater pipeline with up to 1mm leak size. However, the low accuracy due to the insufficient dataset is recognized as a bottleneck and some of the recommendations are suggested for future improvement.