Abstract:

Different from conventional targets, camouflage targets have fuzzy features and complex scale information, resulting in more difficult detection and segmentation. Based on the existing camouflage dataset, a UNet network camouflage image segmentation method combining transfer learning and effective channel attention is proposed. First of all, in view of the problem that the camouflage target feature is difficult to effectively extract, in the down-sampling and up-sampling process of UNet, an effective channel attention mechanism is introduced to increase the feature weight of the effective area without increasing the network parameters. Transfer the visual geometry group (VGG) series network pre-trained on ImageNet to the UNet network to realize feature transfer and parameter sharing, improve the generalization ability of the model, reduce the dependence of the training effect on the dataset, and reduce the training cost. The FocalLoss function is introduced in the training process to increase the weight of difficult samples and increase the attention to difficult samples. Finally get the segmentation through the decoding network result. Tested on the datasets of CHAMELEON, CAMO and COD10K, compared with the original algorithm, the performance indicators of the method proposed have been significantly improved.

Key words: camouflage image, image segmentation, attention mechanism, transfer learning