HWA-DETR: Pedestrian Detection Algorithm Based on High-Width Modulation Attention and Branch Decoupling Reparameters for Improving DETR
Xudong Li, Jingzhi Zhang, Linghui Sun, Chengjie Bai, Xinyao Lv · 2024
In recent years, the Transformer, leveraging its global attention mechanism, has demonstrated powerful performance in visual tasks such as image recognition and object detection. The application of visual Transformers for occlusion pedestrian detection has gained popularity as a promising research direction. Addressing shortcomings in existing detection algorithms and DETR series models for occlusion detection tasks, we propose HWA-DETR, an improved pedestrian detection algorithm. This enhancement incorporates height-width modulated attention and branching decoupling reparameterization from an application perspective. To boost detector performance, we directly process the image using the Transformer. The query inputs are designed with reparameterization of branch decoupling, significantly enhancing the model’s ability to recognize occluded pedestrian objects. During the re-coupling of the two branches, a high-width modulation attention mechanism is employed. This ensures that the model not only focuses on the content features of pedestrian objects but also accurately comprehends and predicts the spatial distributions of pedestrian objects in complex environments, particularly under occlusion conditions. These design improvements effectively enhance the model’s capability to handle occluded pedestrian detection with practical applications, achieving excellent detection performance on the occlusion subset of three datasets: Caltech, CityPersons, and EuroCityPersons. Additionally, extension experiments on two datasets, Caltech, CityPersons, demonstrate good robustness and generalization.