A German high tech company wanted to implement a solution for real-time object detection to assist the operators of their autonomous survey robots. Their aim was to find and identify mission critical objects in hard-to-interpret sonar image data.
The client needed a fully automatic detector working in real-time. The supporting infrastructure should enable the client's customers to train a model with user specific object types, and to deploy the model on their edge devices.
Our cloud-based data management and machine learning platform ensures continuous delivery of individually trained models. A module for embedded systems for edge devices enables running detectors which are configured and trained for the use case.
A highly scalable pipeline for training detection models and an AI IoT solution enable the detection of user specific objects.
Surveying and search operations are carried out worldwide in lakes, canals or coastal waters to detect objects hidden underwater: scrap metal in shipping routes or old military loads, such as aerial bombs or underwater mines. To support these search operations, Evologics developed the Sonobot 5, one of the fastest floating sonar USVs (unmanned surface vessels) in the world. Among other things, it supports the police and coast guard in locating and recovering drowned people.
EvoLogics is a hightech company based in Berlin with strong maritime bionics and robotics R&D experience. They offer solutions for underwater communication and monitoring, including the Sonar USV with the highest speed on water worldwide. This USV should find and identify mission critical objects assisted by real-time object detection.
Autonomous robotic sonar surveys produce large amounts of complex, difficult to interpret sonar data. Points of interest (POI) are obscured by background scatter and noise, hidden in mountains of data.
Moreover, survey missions take place off-grid with limited hardware and limited connection to data processing infrastructure.
How do we assist the survey operators in finding POIs? Can the Bot scan interesting positions automatically from different angles? How can we train and deploy models to support the search for these limited edge devices, and with user specific object types?
We developed a web portal that manages the clients' models, data, and updates for their embedded devices. In this platform the clients can configure and train customized models, choosing their ideal trade-off between speed and accuracy, sharing communal data or keeping their data confidential.
During the survey missions the POIs are detected and highlighted live and in real-time. The survey operators can also do the same anytime with replay data.
They can also set confidence filters for each detected class and optimize for clustered or solitary objects at detection time.
The updates for the models and detectors are modular, saving bandwidth and providing flexibility.
We finetuned a state-of-the art object detection algorithm for objects in sonar images.