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<p>With the continuous development of deep learning, generic object detection techniques are becoming increasingly mature. However, in densely populated scenes where pedestrian density is high and occlusion is severe, the performance of generic object detectors is not ideal. In dense scenes, features from the corner regions could help detectors achieve higher performance, yet existing generic detectors often overlook this aspect. To address the above issues, a plug-and-play Corner Feature Regulate Network, named as CFRN, is proposed in this paper. It extracts the corner features from the deep-layer feature maps of Feature Pyramid Network (FPN) through the Explicit Visual Center (EVC) module. Then the deep layer corner features are utilized to adjust shallow-layer features to ensure that all feature maps contain crucial corner feature essential for dense scenes. Additionally, to address the issue of redundant features which introduced by using bilinear interpolation for multi-scale upsampling in the CFRN, the Redundant Feature Suppression Module (RFSM) is proposed by using ScConv to extract redundant attention from the feature maps of CFRN. This module could reduce the redundant features introduced by using bilinear interpolation in CFRN effectively. The experiment results on the CrowdHuman, CityPersons and COCOPerson show that compared to the Sparse R-CNN, the proposed method improves by 0.7% on AP, decreases by 0.2% on <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(MR^{-2}\)</EquationSource> </InlineEquation>, and improves by 1.1% on JI. Code is available at <a href="https://github.com/davidsmithwj/CS-CS-RCNN">https://github.com/davidsmithwj/CS-CS-RCNN</a>.</p>

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Corner Sparse R-CNN for Pedestrian Detection in Dense Scenes

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Jun Wang,
  • Cong Wu,
  • Lei Wan,
  • Xin Zhang,
  • Shuaiqi Liu

摘要

With the continuous development of deep learning, generic object detection techniques are becoming increasingly mature. However, in densely populated scenes where pedestrian density is high and occlusion is severe, the performance of generic object detectors is not ideal. In dense scenes, features from the corner regions could help detectors achieve higher performance, yet existing generic detectors often overlook this aspect. To address the above issues, a plug-and-play Corner Feature Regulate Network, named as CFRN, is proposed in this paper. It extracts the corner features from the deep-layer feature maps of Feature Pyramid Network (FPN) through the Explicit Visual Center (EVC) module. Then the deep layer corner features are utilized to adjust shallow-layer features to ensure that all feature maps contain crucial corner feature essential for dense scenes. Additionally, to address the issue of redundant features which introduced by using bilinear interpolation for multi-scale upsampling in the CFRN, the Redundant Feature Suppression Module (RFSM) is proposed by using ScConv to extract redundant attention from the feature maps of CFRN. This module could reduce the redundant features introduced by using bilinear interpolation in CFRN effectively. The experiment results on the CrowdHuman, CityPersons and COCOPerson show that compared to the Sparse R-CNN, the proposed method improves by 0.7% on AP, decreases by 0.2% on \(MR^{-2}\) , and improves by 1.1% on JI. Code is available at https://github.com/davidsmithwj/CS-CS-RCNN.