A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks
Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target de...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/45 |
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| Summary: | Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target detection scheme (DDTDS) is proposed for segregating and identifying camouflaged objects. First, the image is segmented using textural pixel patterns for which the linear differentiation is performed. Convolutional neural learning is used for training the regions across pixel distribution and pattern formations. The neural network employs two layers for linear training and pattern differentiation. The differentiated region is trained for its positive rate in identifying the region around the target. Non-uniform patterns are used for training the second layer of the neural network. The proposed scheme pursues a recurrent iteration until the maximum segmentation is achieved. The metrics of positive rate, detection time, and false negatives are used for assessing the proposed scheme’s performance. |
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| ISSN: | 2673-4591 |