Interference Object Detection using TensorFlow Lite and Transfer Learning for Android Devices

Abstract

With the rapid evolution of machine learning and artificial intelligence faster and more robust network architectures are developed. This is possible due to the increase in computational power, improved algorithms and the creation of large scale annotated datasets. Re-purposing these state of the art networks using transfer learning allows for customized models to be created and applied to niche problems. In this paper, we create an object detection application able to detect interference points in anechoic testing chambers. The application runs detection on a mobile device using networks created with TensorFlow Lite. Utilizing the detection result the application can give advice on how to improve the installation in the testing chamber and can thus enforce a baseline for how installations are conducted increasing the repeatability of tests. The end product is an android application running on a mobile device able to detect interference points in 13 FPS for two different testing chambers. The two object detection networks used achieved a mean average precision score of 0.8765 and 0.8650 and a average recall score of 0.7212 and 0.6997 respectively.