Target classification of multislit streak tube imaging lidar based on deep learning.

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Abstract

To reduce the impact of the image reconstruction process and improve the identification efficiency of the multislit streak tube imaging lidar (MS-STIL) system, an object classification method based on the echo of the MS-STIL system is proposed. A streak image data set is constructed that contains a total of 240 common outdoor targets in 6 categories. Additionally, the deep-learning network model based on ResNet is chosen to implement streak image classification. The effects of two classification methods based on streak images and reconstructed depth images are compared. To verify the maximum classification capability of the proposed method, the recognition effects are investigated under 6 and 20 classes. The results show that the classification accuracy decreases from 99.42% to 67.64%. After the data set is expanded, the classification accuracy improved to 85.35% when the class number of the target is 20.