Study on the Detection Method of Fresh Level of Spanish Mackerel Based on Visual Image
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Abstract:
Freshness of Spanish mackerel is an important factor to evaluate its quality. In order to improve the accuracy of Spanish mackerel freshness detection, a visual image-based method was established. The research object of this work is 30 Spanish mackerel purchased from a seafood market. Spanish mackerel visual images were selected by an acquisition system. The region filling algorithm and morphological opening operation were used to fill and remove the binary image of fish segmented by Otsu method. Noise, merge the uphill method and region growing method were used to segment fish eye region. The global dynamic threshold segmentation method was used to segment fish gill image. When extracting image features, the color features of fish body, fish eye and fish gill image were extracted by using the gray mean of R, G and I components of image, and the area of fish eye center area was extracted by using G component. The image features were input into Neuro Shell 2 neural network discriminant model to detect freshness of marine Spanish mackerel effectively. The average accuracy of Spanish mackerel fresh method detection was 98.28%. The highest accuracy of detection of freshness was obtained by using the fisheye center area + color grayscale average characteristics of Spanish mackerel. And detecting the accuracy in determination of the freshness of Spanish mackerel of different death time, was s 95%. Results showed that this detection of freshness of Spanish mackerel could provide a theoretical basis for seafood detection.
