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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.