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Object detection on drone images with low-latency is an important butchallenging task on the resource-constrained unmanned aerial vehicle (UAV)platform. This paper investigates optimizing the detection head based on thesparse convolution, which proves effective in balancing the accuracy andefficiency. Nevertheless, it suffers from inadequate integration of contextualinformation of tiny objects as well as clumsy control of the mask ratio in thepresence of foreground with varying scales. To address the issues above, wepropose 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 theglobal contextual ones, and then designs an adaptive multi-layer maskingstrategy to generate optimal mask ratios at distinct scales for compactforeground coverage, promoting both the accuracy and efficiency. Extensiveexperimental results on two major benchmarks, i.e. VisDrone and UAVDT,demonstrate that CEASC remarkably reduces the GFLOPs and accelerates theinference procedure when plugging into the typical state-of-the-art detectionframeworks (e.g. RetinaNet and GFL V1) with competitive performance. Code isavailable at https://github.com/Cuogeihong/CEASC.

Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images