Detection of small birds in large images by combining a deep detector with semantic segmentation
Akito Takeki, Tu Tuan Trinh, Ryota Yoshihashi, Rei Kawakami, Makoto Iida, Takeshi Naemura · 2016
This paper tackles the problem of bird detection in large landscape images for applications in the wind energy industry. While significant progress in image recognition has been made by deep convolutional neural networks (CNNs), small object detection remains a problem. To solve it, we follow the idea that a detector can be tuned to small objects of interest and semantic segmentation methods can be complementary used to recognize large background areas. Specifically, we train a CNN-based detector, fully convolutional networks, and a superpixel-based semantic segmentation method. The results of the three methods are combined by using support vector machines to achieve high detection performance. Experimental results on a bird image dataset show the high precision and effectiveness of the proposed method.