Automatic exposure correction algorithm for online silkworm pupae (Bombyx mori) sex classification

Abstract

In the process of online sex identification and sorting of silkworm pupae by machine vision technology, the collected images of silkworm pupae gonad suffer underexposure or overexposure due to the biological characteristics of silkworm pupae and the mechanism movement. As a result, the partial texture characteristics of silkworm pupae gonad are not obvious and thus lead to a large error for the subsequent identification of male and female pupae. In view of the overexposed or underexposed problem existed in online image collecting of silkworm pupae gonad, an algorithm model is proposed in this paper. It can automatically determine whether the silkworm pupae gonad image has exposure problem according to the distribution law of gray values. Moreover, an extra correction model based on the combination of the Retinex (retina and cortex) model and the weighted least squares method is put forward to correcting the exposure of gonadal images. The experiment results demonstrate that the proposed algorithm not only can find the silkworm pupae gonad image with exposure problem completely and accurately, but also make the restored gonadal texture clearer. Furthermore, this algorithm has a good effect on the exposure correction of other objects. After the original data sets are processed by the algorithm, the correct sex identification rate in the classification network based on Resnet50 training reaches 90.52%. It is 7.08% higher than the original silkworm pupae gonad images, which can verify the effectiveness of the algorithm. And the recovery time of a single image is within 0.3 s, which can meet the needs of online automatic identification. The proposed algorithm is of great significance for the online male and female identification of silkworm pupae based on machine vision.