  Object detection on drone images with low-latency is an important but
challenging task on the resource-constrained unmanned aerial vehicle (UAV)
platform. This paper investigates optimizing the detection head based on the
sparse convolution, which proves effective in balancing the accuracy and
efficiency. Nevertheless, it suffers from inadequate integration of contextual
information of tiny objects as well as clumsy control of the mask ratio in the
presence of foreground with varying scales. To address the issues above, we
propose a novel global context-enhanced adaptive sparse convolutional network
(CEASC). It first develops a context-enhanced group normalization (CE-GN)
layer, by replacing the statistics based on sparsely sampled features with the
global contextual ones, and then designs an adaptive multi-layer masking
strategy to generate optimal mask ratios at distinct scales for compact
foreground coverage, promoting both the accuracy and efficiency. Extensive
experimental results on two major benchmarks, i.e. VisDrone and UAVDT,
demonstrate that CEASC remarkably reduces the GFLOPs and accelerates the
inference procedure when plugging into the typical state-of-the-art detection
frameworks (e.g. RetinaNet and GFL V1) with competitive performance. Code is
available at https://github.com/Cuogeihong/CEASC.
