An infrared small vehicle target detection method based on deep learning

Infrared small vehicle target detection plays an important role in infrared search and tracking systems applications. The target detection methods based on deep learning are developing rapidly, but the existing approaches always perform poorly for the detection of small target. In this study, we propose an improved SSD(Single Shot MultiBox Detector) to improve the detection performance of infrared small targets from three aspects. First of all, we recommend using the stride convolution layer to replace the 3~6 maximum pooling layers in the original algorithm; second, design a shallow feature layer information enhancement module, semantically fusing the feature maps of the shallow feature layer and the deep feature layer, and using a new pyramid structure to detect the target; third, introducing residual unit and use the MSRA function to initialize the weights of the neurons in each layer at the beginning of training. To evaluate the Infrared-SSD proposed in this paper, the infrared vehicle data set created by this team was used to train and test the model. Experimental results show that Infrared-SSD has higher accuracy than the original SSD algorithm. For an input of 300pixel×300pixel, Infrared-SSD got a mAP(mean Average Precision) test score of 82.02%.