Abstract: Existing object detection algorithms have problems with false positives and false negatives when detecting small hats in complex scenes. In this paper, a multi-stage adaptive hat detection algorithm (MAHD) is proposed. Firstly, a region proposal network (MA RPN) based on adaptive convolution is constructed, and the features of anchors are refined through multiple stages to improve the algorithms ability to recognize targets in complex backgrounds. Then, an adaptive sampling strategy is used to dynamically allocate positive and negative samples, and the focus loss function is combined to guide the training of MA RPN and improve the detection accuracy of small targets. Finally, experiments are conducted on a self-built HAT4.5k dataset. The results show that compared with the Grid R-CNN algorithm, the proposed algorithm improves AP by 2.6% and APS by 5.1%. The detection performance of small targets is further verified on the open-source VisDrone-DET 2019 dataset, demonstrating the feasibility and effectiveness of the proposed algorithm.

Key words: hat detection, adaptive sampling, adaptive convolution, Grid R-CNN, Focal Loss