  Tiny object detection has become an active area of research because images
with tiny targets are common in several important real-world scenarios.
However, existing tiny object detection methods use standard deep neural
networks as their backbone architecture. We argue that such backbones are
inappropriate for detecting tiny objects as they are designed for the
classification of larger objects, and do not have the spatial resolution to
identify small targets. Specifically, such backbones use max-pooling or a large
stride at early stages in the architecture. This produces lower resolution
feature-maps that can be efficiently processed by subsequent layers. However,
such low-resolution feature-maps do not contain information that can reliably
discriminate tiny objects. To solve this problem we design 'bottom-heavy'
versions of backbones that allocate more resources to processing
higher-resolution features without introducing any additional computational
burden overall. We also investigate if pre-training these backbones on images
of appropriate size, using CIFAR100 and ImageNet32, can further improve
performance on tiny object detection. Results on TinyPerson and WiderFace show
that detectors with our proposed backbones achieve better results than the
current state-of-the-art methods.
