<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15039984 |
| Date | 23 July 2021 |
| Creators | Tahrir Ibraq Siddiqui (11173185) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Intelligent_Collision_Prevention_System_For_SPECT_Detectors_by_Implementing_Deep_Learning_Based_Real-Time_Object_Detection/15039984 |
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