Railway foreign objects tracking detection based on spatial location and feature generalization enhancement
There are factors of complex environments,target occlusion,and others.These factors lead to the lack of detection and low detection accuracy of existing depth learning foreign object tracking algorithms.A railway foreign object tracking technique based on spatial location and feature generalization enhancement is proposed to address the issues with the current deep learning video tracking system.The multi-scale cascaded GhostNet network is used to improve the feature extraction ability of the model.The infrared features are enhanced by spatial location and feature generalization module.The module combined with infrared foreign object spatial location and generalization morphology.The detection accuracy of the network is enhanced.The detection anchor size,target kind,and confidence of infrared railway foreign materials are obtained by using the pyramid prediction network.The DeepSORT tracking algorithm which improved category and confidence combined with Kalman filtering and the Hungarian algorithm is used to track railway foreign objects in an infrared weak light environment.The experimental results show that the tracking precision of the proposed algorithm for infrared targets reaches 83.3%,and the average detection rate of the proposed method is 11.3 frames per second.Compared with the comparison method,the proposed algorithm has good performance for tracking railway foreign objects in infrared weak light scenes.
万方数据
维普
