The fully convolution network is a very powerful visual model that can be used to extract features in an image. We improved a network model that can be used for end-to-end, pixel-to-pixel training to extract target motion trajectories in infrared images. The dataset used in our training comes from the simulation dataset produced by the public infrared dataset combined with the simulation trajectory. In order to enhance the model’s robustness, we add the pepper and salt noise and white noise to the simulated image, and use image augmentation to increase the number of the image. We achieved highly train and test accuracy in our simulation dataset.
The robust small and dim target detection is a key technique for infrared search and track system, and it is still a challenging task due to the complex scenarios and noise. An approach based on total variation regularization and principal component pursuit (TV-PCP) method is proposed to achieve robust target detection performance in non-smooth and non-uniform scene, because total variation (TV) regularization could describe the inner smooth and crisp edges of background. Nevertheless, there are still two drawbacks: 1) TV-PCP model only considers the spatial information of single frame and 2) the vanilla nuclear norm adopted in TV-PCP model is only suitable for the scenarios of sufficient edge samples, which would cause some sparse background residuals and increase the false alarm rate. Inspired by this, a novel small target detection approach based on a new reweighted infrared patch image (IPI) model and TV regularization is proposed by utilizing both spatial and temporal information. Then, the TV regularization and weighted nuclear norm minimization are adopted to separate the target and background. For the low-rank background part, we adopt the weighted nuclear norm and TV regularization to describe the smoothness. For sparse target part and noise part, we use the reweighted l1 norm and Frobenius norm term to characterize, respectively. Finally, the proposed model can be solved efficiently by the Alternating Direction Method of Multipliers (ADMM) method. Extensive experiments demonstrate that the background suppression ability and target detection probability of the proposed method is better than the other competitive methods.