A Robust Approach for Hair Contaminant Detection in Papadam Using Transfer Learning
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Abstract
Hair contamination in food products is a serious challenge that impacts quality, safety, and consumer trust. Conventional hair detection techniques often fall short in effectively identifying hair particles, demanding more advanced solutions. This research aims to design and develop a robust framework to correctly detect hair in papadam using image processing and deep learning techniques. The approach starts with an extensive preprocessing pipeline, including grayscale conversion, Wiener filtering for noise reduction, Canny edge detection to highlight edges, and contour detection to extract borderline details, followed by image masking to isolate areas of interest. Pre-trained InceptionV3 transfer learning model is applied for classification, where the initial layers are frozen to preserve learned features, and custom layers are fine-tuned specifically for hair detection. The model incorporates GlobalAveragePooling2D and dense layers with ReLU activation. The proposed approach attained an accuracy of 97.18% in detecting hair contaminants in papadam, validated through real-world datasets. This research highlights the effectiveness of combining comprehensive preprocessing with deep learning to enhance quality, particularly for automatic hair detection in papadam.
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