Vehicle Target Detection Based on Deep Self-encoding Network with TDA

Aiming at the problem of poor real-time performance and low accuracy of vehical detection in snowy environment, a deep self-encoding network vehicle target detection method with Topological Data Analysis is proposed. The method converts the image of the monitoring video frame into point cloud data; extracts the point cloud data of the vehicle target by segmentation and processes the point cloud data of the vehicle target by using the topology data analysis; using the quantized topology data analysis The simplicial complex representation of the vehicle target data is used as an input sample to train the depth self-encoding network, and the last two layers of the stack self-encoding structure are used as outputs to construct the vehicle target feature model, and the softmax classification layer is input through the fully connected layer. The experimental results show that the method can effectively detect vehicle targets in snowy complex environment and improve both accuracy and speed.