Automatic ladybird beetle detection using deep-learning models.

Automatic ladybird beetle detection using deep-learning models.

Blog Article

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics.Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system.First, an image processing module composed of a saliency map representation, simple Mirrror linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image.Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output.

This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project.The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks.These successful results enable the proposed detector as a valuable tool for helping specialists in Baja Bags the ladybird beetle detection problem.