基于Faster R-CNN的密集人群检测算法

为提高拥挤场景下的人群检测准确率，提出一种基于改进Faster R-CNN的密集人群检测算法。首先，在特征提取阶段添加空间与通道注意力机制，使用加强的双向特征金字塔网络（S-BiFPN）替代原网络中的多尺度特征金字塔（FPN），使网络对重要特征进行自主学习并加强对图像深层特征的提取；其次，引入多实例预测（MIP）算法对实例进行预测，以避免模型对拥挤场景下的目标造成漏检；最后，对模型中的非极大值抑制（NMS）进行优化，并额外增设一个交并比（IoU）阈值，以对检测结果的干扰项进行精确抑制。在开源的密集人群检测数据集上进行测试的结果显示，相较于原Faster R-CNN算法，所提算法的平均精度（AP）提升5.6%，Jaccard指数值提升3.2%。所提算法具有较高检测精度和稳定性，可以满足密集场景人群检测的需求。

Dense crowd detection algorithm based on Faster R-CNN

In order to improve the accuracy of crowd detection in crowded scenes， a dense crowd detection algorithm based on improved Faster Region-based Convolutional Neural Network （Faster R-CNN） was proposed. Firstly， the spatial and channel attention mechanisms were added to feature extraction stage and Strong-Bidirectional Feature Pyramid Network（S-BiFPN） was used to replace the multi-scale Feature Pyramid Network （FPN） in the original network， so that the network was able to autonomously learn important features and the extraction of deep image features was strengthened. Secondly， Multi-Instance Prediction （MIP） algorithm was introduced to predict instances， thus avoiding the model’s missed detection of targets in crowded scenes. Finally， Non-Maximum Suppression （NMS） in the model was optimized， and an additional Intersection over Union （IoU） threshold was added to accurately suppress the interference items of the detection results. Experimental results on the open source dense crowd detection dataset show that compared with the original Faster R-CNN algorithm， the proposed algorithm has the Average Precision （AP） increased by 5.6%， and Jaccard index value increased by 3.2%. The proposed algorithm has high detection precision and stability， which can meet the needs of crowd detection in dense scenes.