Dolphin Recognition with Adaptive Hybrid Saliency Detection for Deep Learning Based on DenseNet Recognition
Journal
2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018
Pages
455-458
Date Issued
2019
Author(s)
Abstract
Dolphin identification is important for wildlife conservation. Since identifying dolphins from thousands of images manually takes tremendous time, it is important to develop an automatic dolphin identification algorithm. In this paper, a high accurate deep learning based dolphin identification algorithm is proposed. We presented an advanced approach, called hybrid saliency method, for feature extraction and efficiently integrate several well-known techniques to make dolphins distinguishable. With the proposed techniques, we can avoid the background part (e.g.The sea water) to affect the identification results, which is usually a problem of most convolutional neural network based methods. Simulations show that the proposed algorithm can well identify a dolphin in most cases and it can achieve the accuracy rate of 85% even if there are 40 dolphins to be distinguished. © 2018 IEEE.
Subjects
computer vision; convolutional neural networks; marine vertebrate; photo-identification; saliency map
SDGs
Other Subjects
Computer vision; Conservation; Convolution; Dolphins (structures); Image processing; Neural networks; Seawater; Accuracy rate; Convolutional neural network; Identification algorithms; marine vertebrate; Photo identification; Saliency detection; Saliency map; Wildlife conservation; Deep learning
Type
conference paper