Docker Vision is increasing operational efficiency and reducing cost for port/railway/enterprise operators. The team has built Machine Learning based Computer Vision algorithms to enable basic cameras to perform advanced surveillance solutions across industries. Some use cases include container number identification and automated container inspection, vehicular number plates inspection, factory asset / employee management amongst others.
Current automated inspection solutions across industries are time consuming, expensive and not personalized. This leads to inefficient use of resources and inability to optimize processes despite digital transformation spends, leading to business loss.
Rise in digitization is leading to increased demand for automation solutions across ports / railways/ enterprises, with massive spending on use cases like surveillance. Docker’s use of a hardware agnostic software first approach is innovative and scalable. They have trained the model over 2 years with extensive datasets to achieve industrial grade efficiency on off the shelf cameras. Built in India, the software is extremely affordable and customizable, this enabled is to garner a healthy pipeline of POCs and paid implementations.