Training prototxt file containing the hyperparameter settings for combinations 5 and 15 of optimized training runs.

Plots generated from training logs of combinations 5 and 15 of optimized training runs.

500 of 690 training images used in optimized training runs.

Annotations for 500 of the 690 images used for training.

<p>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. <a>This research work proposes an intelligent real-time object detection system to prevent collisions</a> 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 <i>Caffe</i> version of <i>MobileNet-SSD </i>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.</p>

Demos of deploying caffemodel trained for 16000 iterations after the initial training session in the three scenarios outlined in the paper and a minimum confidence score of 30% for detections.

Demos of deploying combo 5 caffemodel trained for 18000 iterations and combo 15 caffemodel trained for 25000 iterations.