Saury Sensing for Packaging

Abstract

Detecting damages on the body of the saury is important for packaging in the fisheries industry. Moreover, the appearance of a fish is important in fisheries. Fish damage detection relies on manual assembly lines, which are labor-intensive and time-consuming. This paper proposes a novel saury damage detection method that automatically localizes the saury body region, determines the head-tail orientation, and detects damaged parts from the body region. We collected a saury image dataset for training and built a simple naive convolutional neural network (CNN) model without transfer learning for damage discrimination. Seven evaluation metrics, such as accuracy, F1-score, Area under the ROC curve (AUC), and prediction time, were comprehensively considered to select the optimal parameter configuration including the number of layers, number of filters, filter size, and input image size. We also utilized the transfer learning of eight pretrained CNN models to classify the saury into two categories for damage discrimination. Inception-v3 and ResNet-101 achieved the highest accuracy of 98.2% for damage discrimination. Through extensive experiments and discussion of the results, this paper presents clear guidance for choosing pretrained networks for transfer learning in damage detection tasks.