Date of Award
Master of Science
Electrical and Computer Engineering
Arun K. Somani
Detection of objects of interest is a fundamental problem in computer vision. Foreign object detection (FOD) is to detect the objects that are not expected to be appear in certain area. For this task, we
need to first detect the position of foreign objects, and then compute the distance to the foreign objects
to judge whether the objects are within the dangerous zone or not. The three principle sources of difficulty in performing this task are: a) the huge number of foreign objects categories, b) the calculation of
distance using camera(s), and c) the real-time system running performance. Most state-of-art detectors
focus on one type or one class of objects. To the best of our knowledge, there is no single solution that
focuses on a set of multiple foreign objects detection in an integrated manner. In some cases, multiple
detectors can operate simultaneously to detect objects of interest in a given input. This is not efficient.
The goal of our research is to focus on detection of a set of objects identified as foreign object in an
integrated and efficient manner. We design a multi-class detector. Our approach is to use a coarse-tofine strategy in which we divide the complicated space into finer and finer sub-spaces. For this purpose,
data-driven clustering algorithm is implemented to gather similar foreign objects samples, and then an
extended vector boosting algorithm is developed to train our multi-class classifier. The purpose of the
extended vector boosting algorithm is to separate all foreign objects from background. For the task of
estimation of the distance to the foreign objects, we design a look-up table which is based on the area
of the detected foreign objects.
Furthermore, we design a FOD framework. Our approach is to use stereo matching algorithm to
get the disparity information based on intensity images from stereo cameras, and then using the camera
model to retrieve the distance information. The distance calculated using disparity is more accurate
than using the distance look-up table. We calculate the initial distance map when no objects are in the
scene. Block of interest (BOI) is the area where distance is smaller than the corresponding area in the
initial distance map. For the purpose of detecting foreign objects, we use flood fill method along with
noise suppression method to combine adjacent BOI with higher confidence level.The foreign object detection prototype system has been implemented and evaluated on a number of
test sets under real working scenarios. The experimental results show that our algorithm and framework
are efficient and robust.
Lin, Haoyuan, "Foreign object detection (FOD) using multi-class classifier with single camera vs. distance map with stereo configuration" (2015). Graduate Theses and Dissertations. 14565.