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There are deviations in the images of the same target at different angles, so the multi-angle infrared images contain additional classification and identification feature information. Therefore, the multi-angle image acquisition system can effectively solve the problems such as occlusion and small target size that cannot be recognized with high precision. Combined with the multi-angle acquisition system, a multi-angle infrared vehicle target recognition algorithm based on Light-Head R-CNN was proposed. Firstly, inputted the multi-angle infrared images of the same target into the ConvNeXt backbone network for feature extraction; Secondly, in order to reduce the amount of model parameters and realize the lightweight of the network, the traditional convolution in the backbone network was replaced with Ghost Module, which reduced the amount of FLOPs and parameters by about 50%; At the same time, according to the characteristics of infrared vehicle targets, the Light-Head R-CNN target recognition algorithm was improved, and an ultra-lightweight ECA module without dimensionality reduction local cross-channel interaction was added to improve the performance of the network for infrared vehicle target recognition; Finally, the Dempster synthesis rule was used to perform data fusion on the recognition accuracy of images from different angles predicted by the network to obtain the final recognition accuracy. It had been verified that compared with single-angle images, when the number of input angles was 2, the recognition accuracy of the algorithm in the constructed data set was improved by 3.8%; The recognition accuracy reached 90.1%, it was optimal, while the input infrared images were distributed at a certain angle and the number was 5, at the same time the speed achieved 44fps. The results fully demonstrated the feasibility of the proposed algorithm in improving the recognition accuracy, and provided a theoretical and experimental basis for enhancing the performance of the target recognition algorithm.
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