基于DE-YOLO的室内人员检测方法

Indoor Personnels Detection Method Based on DE-YOLO

目标检测的一个重要应用场景是对室内流动人员的检测与定位,为了降低模型的冗余度和提高检测的精确度,因此本文提出一种基于DE-YOLO的室内人员检测方法.通过使用K-means算法对数据集进行聚类,并设计出这种DE-YOLO深度卷积神经网络结构.通过DE-YOLO网络结构中的密集型连接,实现模型大小的压缩和特征信息的复用,最后对提取到的特征进行目标检测.在VOC2012数据集上进行实验表明,新改进的深度卷积网络应用性能有较大的提升.

An important application scenario for target detection is the detection and location of indoor mobile personnel.In this study, we propose an indoor personnel detection method to improve YOLOv3. First, the proposed method clusters the dataset by using K-means algorithm and designs a DE-YOLO deep convolutional neural network structure. Through the dense connection in the DE-YOLO network structure, the compression of the model sizes and the reuse of the feature information are realized. Finally, the target detection is performed on the extracted features. Experiments show that the application of the newly improved deep convolutional network has greatly improved application effect on VOC2012 datasets.