Combining deep features for object detection at various scales: finding small birds in landscape images

Demand for automatic bird ecology investigation rises rapidly along with the widespread installation of wind energy plants to estimate their adverse environmental effect. While significant advance in general image recognition has been made by deep convolutional neural networks (CNNs), automatically recognizing birds at small scale together with large background regions is still an open problem in computer vision. To tackle object detection at various scales, we combine a deep detector with semantic segmentation methods; namely, we train a deep CNN detector, fully convolutional networks (FCNs), and the variant of FCNs, and integrate their results by the support vector machines to achieve high detection performance. Through experimental results on a bird image dataset, we show the effectiveness of the method for scale-aware object detection.

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