针对Tiny YOLOv3算法在扶梯异常行为检测时存在高漏检率和低准确率的问题,提出一种改进的Tiny YOLOv3网络结构用于扶梯异常行为检测。利用K-means++算法对数据集中的目标边框进行聚类,根据聚类结果优化网络的先验框参数,使训练网络在异常行为检测方面具有一定的针对性。利用多层深度可分离卷积提取深层次的语义信息,加深特征提取的网络结构;增加一个尺度用于低层语义信息的融合,改进原有算法预测层的结构;使用GPU进行多尺度训练,得到最优的权重模型,对扶梯异常行为进行检测。实验结果表明,优化后的模型与Tiny YOLOv3相比,平均漏检率减小了22.8%,检测精度提高了3.4%,检测速度是YOLOv3的1.7倍,更好地兼顾了检测的精度和实时性。

Because of the high missing rate and low accuracy of Tiny YOLOv3 algorithm in the detection of abnormal escalator behavior, an improved Tiny YOLOv3 network structure is proposed for the detection of abnormal escalator behavior. K-means++ algorithm is used to cluster the target boundaries in the data set. The a priori parameters of the network are optimized according to the clustering results to make the training network have a certain pertinence in abnormal behavior detection. The network structure of feature extraction is deepened by using multi-layer deep separable convolution to extract deep semantic information. A scale is added to fuse low-level semantic information to improve the structure of the prediction layer of the original algorithm. Finally, the GPU is used for multi-scale training. The optimal weight model is obtained to detect the abnormal behavior of escalators. The experimental results show that compared with Tiny YOLOv3, the optimized model improves the missed detection rate by 22.8%, the detection accuracy by 3.4%, and the detection speed by 1.7 times. It gives better consideration to the accuracy and real-time performance of the detection.