Improving Pest Bird Detection in a Vineyard Environment using Super-Resolution and Deep Learning

Abstract

Pest bird detection, classification, and recognition in vineyard environment are challenging because of their varying shapes, small size, movement, and outdoor environment. Motion is often used to detect flying birds in outdoor environment from video sequences. However, motion detection is sensitive to noise as well as background movement of leaves and give rise to false detection. The high-quality image resolution is desired for performance improvement in pattern recognition and analysis. This work presents the integration of super-resolution technology to enhance quality of small moving objects which were later on classified as birds or false positives using deep learning. Implementation of the super-resolution enhanced the image resolution which offers high pixel density and more details about the scene. With the implementation of super-resolution, the CNN-based classifier received enhanced feature information to perform more informed decision in classifying birds. The classification accuracy shows a significant rise from 70% to more than 90% after resolution enhancement. Results also show that the model trained with combined varying spatial resolution for the same set of images performs almost equally over any spatial resolution.