High-Throughput Online Visual Detection Method of Cracked Preserved Eggs Based on Deep Learning

Abstract

Cracked preserved eggs can easily decay, emit a peculiar smell, and cause cross-infection. The identification of cracked preserved eggs during production suffers from low efficiency and high cost. This paper proposes an online detection and identification method of cracked preserved eggs to address this issue. First, the images of preserved eggs are collected online. Then, each collected image is cut into a single image of the preserved egg, and the images of different surfaces of the same preserved egg are respectively spliced by the sequential splicing scheme and the matrix splicing scheme. Finally, the data sets obtained by the two stitching methods are exploited to establish a deep learning detection model. The experimental results indicate that the MobileNetV3_egg model, an improved version of the MobileNetV3_large model, achieves the best recognition ability for cracked preserved eggs by using the matrix splicing scheme. The accuracy reaches 96.3%, and the detection time for 300 images is only 4.267 s. The proposed method can meet the needs of actual production, and the application of this method will make the identification of cracked preserved eggs more automated and intelligent.