Intelligent Collision Prevention System For SPECT Detectors by Implementing Deep Learning Based Real-Time Object Detection
The SPECT-CT machines manufactured by Siemens consists of two heavy detector heads(~1500lbs each) that are moved into various configurations for radionuclide imaging. These detectors are driven by large torque powered by motors in the gantry that enable linear and rotational motion. If the detectors collide with large objects – stools, tables, patient extremities, etc. – they are very likely to damage the objects and get damaged as well. This research work proposes an intelligent real-time object detection system to prevent collisions between detector heads and external objects in the path of the detector’s motion by implementing an end-to-end deep learning object detector. The research extensively documents all the work done in identifying the most suitable object detection framework for this use case, collecting, and processing the image dataset of target objects, training the deep neural net to detect target objects, deploying the trained deep neural net in live demos by implementing a real-time object detection application written in Python, improving the model’s performance, and finally investigating methods to stop detector motion upon detecting external objects in the collision region. We successfully demonstrated that a Caffe version of MobileNet-SSD can be trained and deployed to detect target objects entering the collision region in real-time by following the methodologies outlined in this paper. We then laid out the future work that must be done in order to bring this system into production, such as training the model to detect all possible objects that may be found in the collision region, controlling the activation of the RTOD application, and efficiently stopping the detector motion.
- Master of Science in Engineering
- Electrical and Computer Engineering
- Fort Wayne