A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection

Abstract

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role.