CrossNet: Computing-Friendly Lightweight Anchor-Free Detector

Abstract

Many scenarios such as edge and mobile scenarios are very sensitive to computing complexity and parameter size. Most object detection models cannot be directly deployed without specific modifications. In this paper, we introduce CrossNet, a compute-friendly lightweight anchor-free detector based on CenterNet. We abandon DCN module and design the network structure according to lightweight design principles. A new sample matching strategy based on neighbor points is adopted for improving its performance greatly. Weighted Focal Loss and Balanced L1 Loss further reduce the performance gap between CrossNet and CenterNet. CrossNet possesses a model size of 4.3MB (12.8× smaller than original CenterNet, respectively) and requires 1.51B operations for inference (11.7× smaller than original CenterNet, respectively) while still achieving mAP of 74.2% on VOC dataset. We hope our work could provide some inspiration for lightweight detector design.