Overlapping Shoeprint Detection by Edge Detection and Deep Learning

Date
2024-07-31
Authors
Li, Chengran
Narayanan, Ajit
Ghobakhlou, Akbar
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
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 and 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.

Description
Keywords
46 Information and Computing Sciences , 4603 Computer Vision and Multimedia Computation , Networking and Information Technology R&D (NITRD) , Machine Learning and Artificial Intelligence , Bioengineering , Clinical Research , 4003 Biomedical engineering , 4603 Computer vision and multimedia computation
Source
Journal of Imaging, ISSN: 2313-433X (Print); 2313-433X (Online), MDPI AG, 10(8), 186-186. doi: 10.3390/jimaging10080186
Rights statement
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).