CNN WITH OPTIMIZED FULLY CONNECTED LAYER BASED INTELLIGENT DEFECT DETECTION SYSTEM FOR FOOD PACKAGE

Abstract

The quality of food packaging plays a significant role in determining the shelf life of consumer products. This is a good preservative that extends the shelf life of food. In some cases, food packaging may contain some defects, such as blow holes, holes, burrs, shrink defects, mold material defects, metal defects, metal defects, etc., which may affect the purity of the food. To avoid these issues, in this paper, an efficient automatic detection system using a deep learning (DL) algorithm is proposed. The proposed approach consists of two stages namely, pre-processing and defect detection.   Initially, the images are collected from the dataset. Median filters are used to remove noise from the images. Once the pre-processing has been completed, the pre-processed images are given to the classifier. Optimised fully connected CNNs are proposed in this paper for classification. With adaptive reptile search optimization algorithm (ARSA), full-connected layer weight parameters are optimized for enhanced performance. The proposed algorithm effectively detects the problem and correctly segments the affected region. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, and precision.