An intelligent approach to identify parasitic eggs from a slender-billed’s nest

Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull’s Nest

“…In support of efforts to protect endangered birds such as the Slender-Billed Gulls, recent research works based on egg visual features have been proposed. These works can be divided into two categories: shallow features-based methods [12]- [15] and deep learning-based methods [16], [17] (see table 1). Indeed, many shallow features have been used to characterize the bird's eggs in order to classify them and identify possible parasitism.…”

“…This can be explained by the unsatisfactory egg characterization using the proposed shallow-the features. Consequently, and due to the high performances of the deep learning models in image classification, recent avian egg classification methods [16], [17] have used these models and given promising results.…”

“…In [16], a slender-billed Gull egg classification method based on Convolution Neural Networks (CNN) has been proposed. Indeed, the egg RGB images have been introduced to the CNN to extract the egg features.…”

“…It has given an egg classification accuracy of 87%. While, using the same CNN model, [17] has reached an egg classification accuracy of 91% and 93% using Softmax and SVM classifiers respectively. This method extracts features from 4 multi-resolution images issued by applying the Discrete Wavelet Transform (DWT) of the egg images.…”

“…This method extracts features from 4 multi-resolution images issued by applying the Discrete Wavelet Transform (DWT) of the egg images. Indeed these two methods [16], [17] have used the CNN model which is considered as a simple deep learning model. In addition, they have been evaluated on a relatively parasitized dataset.…”