Article
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Overlapping Shoeprint Detection by Edge Detection and Deep Learning
Version 1
: Received: 31 May 2024 / Approved: 1 June 2024 / Online: 4 June 2024 (02:52:43 CEST)
A peer-reviewed article of this Preprint also exists.
Li, C.; Narayanan, A.; Ghobakhlou, A. Overlapping Shoeprint Detection by Edge Detection and Deep Learning. J. Imaging 2024, 10, 186. Li, C.; Narayanan, A.; Ghobakhlou, A. Overlapping Shoeprint Detection by Edge Detection and Deep Learning. J. Imaging 2024, 10, 186.
Abstract
In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.
Keywords
object detection; overlapping shoeprint; edge detection; 2-D image processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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