2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 13 , Issue 2 , PP: 136-144, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Suha Dh. Athab 1 * , Abdulamir A. Karim 2
Doi: https://doi.org/10.54216/FPA.130212
This paper presents a tagging model used the Segmentation map as reference regions. The suggested model leverages an encoder-decoder architecture combined with a proposal layer and dense layers for accurate object tagging and segmentation. The proposed model utilizes a pre-trained VGG16 encoder to extract high-level features from input images, followed by a decoder network that reconstructs the image. A proposal layer generates a binary map indicating the presence or absence of objects at each location in the image. The proposal layer is integrated with the decoder output and further refined by a convolutional layer to produce the final segmentation. Two dense layers are employed to predict object classes and bounding box coordinates. The model is trained using a custom loss function that combines categorical cross-entropy loss and means squared error loss. Experimental results demonstrate the effectiveness of the proposed model in achieving accurate object tagging and segmentation.
Tagging , Encoder decoder , Semantic segmentation , Object detection
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