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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MDPI AG
2023-12-01
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| author | Jagadesh Sambbantham Gomathy Balasubramanian Rajarathnam Mohit Tiwari |
| author_facet | Jagadesh Sambbantham Gomathy Balasubramanian Rajarathnam Mohit Tiwari |
| author_sort | Jagadesh Sambbantham |
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| description | 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. |
| format | Article |
| id | doaj-art-5ccc9a7cce9b4ae3bb24e0f5f5d2c52c |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-5ccc9a7cce9b4ae3bb24e0f5f5d2c52c2025-08-20T03:43:02ZengMDPI AGEngineering Proceedings2673-45912023-12-015914510.3390/engproc2023059045A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural NetworksJagadesh Sambbantham0Gomathy Balasubramanian1Rajarathnam2Mohit Tiwari3Department of ECE, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, IndiaDepartment of CSE, Dr.N.G.P Institute of Technology, Coimbatore 641048, Tamilnadu, IndiaDepartment of Mechatronics Engineering, Paavai Engineering College, Namakkal 637018, Tamilnadu, IndiaDepartment of CSE, Bharati Vidyapeeth’s College of Engineering, Delhi 110063, IndiaCamouflaged 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.https://www.mdpi.com/2673-4591/59/1/45camouflaged target detectionconvolution neural networklinear differentiationpattern detectionsegmentation |
| spellingShingle | Jagadesh Sambbantham Gomathy Balasubramanian Rajarathnam Mohit Tiwari A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks Engineering Proceedings camouflaged target detection convolution neural network linear differentiation pattern detection segmentation |
| title | A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks |
| title_full | A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks |
| title_fullStr | A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks |
| title_full_unstemmed | A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks |
| title_short | A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks |
| title_sort | linear differentiation scheme for camouflaged target detection using convolution neural networks |
| topic | camouflaged target detection convolution neural network linear differentiation pattern detection segmentation |
| url | https://www.mdpi.com/2673-4591/59/1/45 |
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