Bird Image Retrieval and Recognition Using a Deep Learning Platform

Abstract

Birdwatching is a common hobby but to identify their species requires the assistance of bird books. To provide birdwatchers a handy tool to admire the beauty of birds, we developed a deep learning platform to assist users in recognizing 27 species of birds endemic to Taiwan using a mobile app named the Internet of Birds (IoB). Bird images were learned by a convolutional neural network (CNN) to localize prominent features in the images. First, we established and generated a bounded region of interest to refine the shapes and colors of the object granularities and subsequently balanced the distribution of bird species. Then, a skip connection method was used to linearly combine the outputs of the previous and current layers to improve feature extraction. Finally, we applied the softmax function to obtain a probability distribution of bird features. The learned parameters of bird features were used to identify pictures uploaded by mobile users. The proposed CNN model with skip connections achieved higher accuracy of 99.00 % compared with the 93.98% from a CNN and 89.00% from the SVM for the training images. As for the test dataset, the average sensitivity, specificity, and accuracy were 93.79%, 96.11%, and 95.37%, respectively.INDEX TERMS Bird image recognition, convolutional neural network, deep learning, mobile app.

Investigation of Different CNN-Based Models for Improved Bird Sound Classification

“…2), which makes imbalance learning worth being investigated. Previously, image data has been investigated for recognizing bird species [12], it it worthwhile fusing both audio and image data for classifying bird species. Another research direction is to build an efficient classification framework for recognizing bird species by intelligently fusing CNN models.…”

Comprehensive Bird Preservation at Wind Farms

“…There have also been attempts to apply identification methods other than by Neural Network, such as Haar Feature Based Cascade Classifier [48], which could give a better individual detection performance in comparison to CNN, but does not perform as well when tasks are conducted on many features [42]. Another method is the Long Short-Term Memory (LSTM) [45], which gives a better performance in bird identification near the moving blades of wind turbines.…”

“…Nevertheless, identification of small birds with CNN-LSTM still requires improvement. Dense CNN [48] reported good identification performance with additional skip connections [46,53], which improve feature extraction. After 100 epochs, CNN with skip connections reach near 99% identification accuracy, when the same CNN architecture without skip connections reaches 89%.…”

A Runway Safety System Based on Vertically Oriented Stereovision

“…Whereas the motion detection algorithms allow the reduction of the computational complexity of the safety system [ 50 ], the application of AI methods allows bird identification [ 48 , 49 ] and the reduction of false positive rates [ 10 ]. From the AI based solutions, the Convolutional Neural Networks (CNNs) [ 51 , 52 , 53 ] outperform other methods, for instance the Haar feature based cascade classifier [ 45 , 54 ] or Long Short-Term Memory (LSTM) [ 48 ]. The most recent studies reported that dense CNN [ 54 ] shows good feature extraction capabilities allowing for bird identification [ 49 , 55 ], and after 100 epochs, the system reaches near 99% accuracy.…”

“…From the AI based solutions, the Convolutional Neural Networks (CNNs) [ 51 , 52 , 53 ] outperform other methods, for instance the Haar feature based cascade classifier [ 45 , 54 ] or Long Short-Term Memory (LSTM) [ 48 ]. The most recent studies reported that dense CNN [ 54 ] shows good feature extraction capabilities allowing for bird identification [ 49 , 55 ], and after 100 epochs, the system reaches near 99% accuracy. Other CNNs, implemented in distributed computing and IoT paradigms, allow the system to ensure 99.8% precision with 99.0% recall in bird identification with real-time performance [ 10 ].…”