Image and Video Annotation for the Shipping Industry
Maritime trade is an extremely competitive and low-margin business. Despite this ever increasing global demand for shipping services means that there are significant potential advantages to be gained from improving efficiency, reliability, and safety in shipping and port operations.
Computer vision based AI systems are now playing a part in this drive for industry improvement. Machine learning models have the capacity to facilitate smoother processes en route and at loading terminals, however, these advancements are dependent on access to precisely labeled image and video training data.
In order for computer vision models to understand the world of maritime trade they must be trained with many thousands of accurately annotated images. This blog will detail some of the exciting use cases for AI in the shipping industry, and how they are being supported by annotation services.
Image Recognition for Safety
Navigating busy shipping lanes and congested ports can present significant safety challenges. Collisions can occur as large vessels and smaller craft interact. Busy ship pilots may miss obstacles or other ships due to task saturation. Machine learning powered image recognition systems can help to support ship operators and ensure that major accidents are avoided.
AI enabled cameras can identify smaller vessels in the surrounding area, and warn maritime pilots of potential dangers. This technology is particularly useful in low visibility conditions, such as bad weather or operating at night.
Professional annotation services, like Keymakr, are helping to support image recognition development by providing accurately annotated images and videos. Using labeling techniques, such as bounding boxes, experienced annotators can create datasets that make the real world intelligible to AI models.
Unmanned Vessels
The potential of autonomous vessels is enormous. AI powered ships could safely navigate across the globe, reducing shipping costs and accelerating trade. Autonomous ships need to be able to navigate using satellites and route planning data, but they will also be required to make real time decisions based on incoming image and video information.
Machine learning can empower unmanned vessels to respond intelligently to sea states and obstacles, securing the viability of this emerging technology.
Again, precisely labeled image datasets are key to this effort. Managed teams of annotators can apply semantic segmentation annotation techniques to a variety of images. Sea conditions can be separated from objects and vessels using this method. Allowing autonomous ships to achieve a granular understanding of a complex world.
Automation for Shipping Terminals
Computer vision has perhaps the most applications after vessels have reached their destinations. Ports and terminals are beginning to appreciate the potential of AI systems and their capacity to streamline cargo loading and unloading.
Port equipment, such as cranes and guided vehicles, are currently almost exclusively controlled remotely by experienced operators. Automation can help to support operators by reducing the amount of inputs required per minute, ensuring both efficiency and safety at terminals.
AI powered cranes can identify containers that need to be moved and guide the machinery to where it needs to be. Automated vehicles can also move containers across ports to waiting trucks for transportation.
Annotated video is crucial for training the AI systems that allow for automated port operations. However, labeling large quantities of video is a time consuming and labour intensive challenge. Annotation services can help manage labeling workloads and ensure that port and terminal AI projects are receiving the right amount of the right data.
Image and Video Annotation for Maritime AI
Keymakr takes advantage of an in-house team of experienced annotators overseen by quality driven managers to support emerging developments in machine learning for shipping applications. Contact a team member to book your personalized demo today.
