An Evaluation of Deep Learning Methods for Small Object Detection

by Nhat-Duy Nguyen, Tien Do, Thanh Duc Ngo

Small Object detection is an interesting topic in computer vision. With the rapid development in deep learning, it has drawn attention of several researchers with innovations in approaches to join a race. These innovations proposed comprised region proposals, divided grid cell, multiscale feature maps, and new loss function. As a result the performance of object detection has recently had significant improvements. However, most of the state of the art detectors, both in one stage and two stage approaches, have struggled with detecting small objects.

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Published January 1, 2020

Reviews

[John Lister]

Good journal that covers the complicated and confusing concept of small Object detection. It certainly helped me decide the best models to use for an application I am building so I am happy with the work they did on this.