Version 1
: Received: 29 January 2024 / Approved: 30 January 2024 / Online: 31 January 2024 (01:51:23 CET)
Version 2
: Received: 11 April 2024 / Approved: 15 April 2024 / Online: 15 April 2024 (04:30:36 CEST)
How to cite:
Van der Burg, E.; Toet, A.; Perone, P.; Hogervorst, M.A. A Convolutional Neural Network as a Potential Tool for Camouflage Assessment. Preprints2024, 2024012072. https://doi.org/10.20944/preprints202401.2072.v1
Van der Burg, E.; Toet, A.; Perone, P.; Hogervorst, M.A. A Convolutional Neural Network as a Potential Tool for Camouflage Assessment. Preprints 2024, 2024012072. https://doi.org/10.20944/preprints202401.2072.v1
Van der Burg, E.; Toet, A.; Perone, P.; Hogervorst, M.A. A Convolutional Neural Network as a Potential Tool for Camouflage Assessment. Preprints2024, 2024012072. https://doi.org/10.20944/preprints202401.2072.v1
APA Style
Van der Burg, E., Toet, A., Perone, P., & Hogervorst, M.A. (2024). A Convolutional Neural Network as a Potential Tool for Camouflage Assessment. Preprints. https://doi.org/10.20944/preprints202401.2072.v1
Chicago/Turabian Style
Van der Burg, E., Paola Perone and Maarten A. Hogervorst. 2024 "A Convolutional Neural Network as a Potential Tool for Camouflage Assessment" Preprints. https://doi.org/10.20944/preprints202401.2072.v1
Abstract
Camouflage evaluation typically involves human visual search and detection experiments that are time-consuming and expensive. Hence, there is a need for models that compute camouflage effectiveness from digital imagery. Convolutional neural networks are a powerful tool for automatic object detection and recognition. We investigated whether such a network (YOLO) can also provide a measure of camouflage effectiveness that is related to human perception. To this end, human performance measures of camouflage effectiveness such as detection time and target conspicuity were obtained in observer experiments and compared with the performance of the (standard, i.e. pre-trained) YOLO-V4-tiny algorithm. YOLO provides the probability that a detected object is correctly classified, and this is adopted as our measure of camouflage effectiveness. We compared the YOLO-predicted classification probability for a person in camouflage clothing moving through rural and urban scenes to human detection performance. Overall, camouflage effectiveness predicted by YOLO correlates with human observer performance. At close distances, YOLO’s classification performance appears sensitive to high-contrast shape-breaking elements of a camouflage pattern. This suggests that YOLO may be adapted to assess camouflage effectiveness for a wide range of applications, such as evaluating or optimizing one’s signature and predicting optimal hiding locations in a given environment. Further research is required to fully establish YOLO’s limitations and applicability for this purpose.
Computer Science and Mathematics, Computer Vision and Graphics
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.
