GroceryNet grocery product image analysis using multicolor dual-input parallel CNN for simultaneous semantic segmentation and classification - Yao, Shaw Jie A.; Yusiong, John Paul T. (Tacloban City Division of Natural Sciences and Mathematics, University of the Philippines Tacloban College, 2025)
This research explores the application of deep learning techniques for grocery item image segmentation and classification, with multicolor dual-inputs such as RGB with XYZ, XYZ with LAB, and so on in parallel CNNs: Wherein, the U-Net model variation of the CNN comes into the picture to employ these two tasks simultaneously toward the outputs. The Hierarchical Grocery Store dataset, which includes a diverse set of fruit, vegetable, and packaged products (1), along with manually generated segmentation masks, serves as the basis for the experiments. By comparing models. that use single-color inputs (eg., RGB) with those that incorporate multiple color spaces, this paper investigates the potential benefits of using multicolor dual-input parallel channels. This approach aims to improve both segmentation and classification performance, in terms of being able to address limitations in the existing models that treat these two tasks separately. Thus, experimental results demonstrate that using multicolor dual-inputs achieves better performance in terms of both accuracy and robustness when compared to those using only a single-color space. Particularly, with the RGB + XYZ dual-color space combination and inclusion of pre-trained RosNeXt50_32x4d encoders, the model was able to achieve the following performance metrics: 93.48% mIoU, 96.09% accuracy, 96.09% precision, 97.18% recall, 96.63% Fl-score for segmentation and 98.74% Top-1 and 99.99% Top-5 classification accuracy, highlighting the model's competence in this multitask setup.
Undergraduate thesis (Bachelor of Science in Computer Science) -- University of the Philippines, Tacloban.
Includes bibliographical references.
Keywords: Deep learning