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The details and shape information of the target are effectively highlighted in the polarized image, which is more conducive to target detection. At present, the influence of different polarization parameters on the target detection task has not been studied in depth. There are problems that the ways of characterization of polarization parameter is so rich and varied that the polarization parameter is difficult to select, when we utilize the convolutional neural network (CNN) model to detect images obtained by polarimetric systems. In response to this problem, this paper uses the convolutional neural network (CNN) model to train a variety of polarized parametric images in many different input configuration for experimental comparison. Firstly, the sample data is acquired using a focal plane polarized camera, and the database is expanded using a data enhancement strategy to establish a polarized image data set. Then, different image input configurations are used as the training set, and the convolutional neural network (CNN) pre-training model is iteratively trained and fine-tuned to obtain the target detection model of the polarized image. Finally, in order to evaluate the performance of the model, the experimental trials are quantified by mean average precision (mAP) and processing time, and the influence of different polarization image input configurations on the detection model is analyzed. The experimental results show that compared with the model trained by single channel image configuration, the model trained by threechannel image configuration has better performance, but there is no obvious difference between models trained by different three-channel configurations.
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