Efficient spatial pyramid for object detection

One-stage object detectors emerge as a trade-off between detection accuracy and speed. However, they do not exploit long-range context relationship and their performance can easily drop in complex scenes. In this work, we propose efficient spatial pyramid based on graph model of Laplacian with a novel graph correlation filter. This filter is designed to measure symmetric uncertainty among features to learn long-range context relationship, while preserving important features. Furthermore, we employ Winograd algorithm to reduce floating point operations significantly without decreasing detection performance, by trading-off costly multiplication operations with more additions. They enable an efficient object detection. Extensive experiments were conducted on two challenging object detection datasets, COCO and KITTI. The proposed network was compared to state-of-the-art efficient object detectors, MobileNet-SSD Lite, YOLO, MobileVIT, Tiny DSOD, and EfficientDet. Detailed convergence proof and training epoch analysis provide strong support and evidence for the achieved results improving overall detection accuracy in complex scenes with less computational resources. • Hierarchical Laplacian-driven design capturing both fine-grained and global context. • Symmetrical uncertainty filter for capturing long-range feature interactions. • Accelerated convolutional layers via Winograd minimal filtering. • Thorough empirical validation and ablation study on COCO and KITTI datasets. • ESP attains the best-in-class accuracy-to-efficiency trade-off.