The border regression is a key technique of the regional convolution neural network (CNN) to locate the target. However, it relies on the border label information of a large number of sample data. Therefore, it is inefficient to generate the training sample set, and the location of the target is also inaccurate. For this, a novel target detection method based on the CNN and the particle search is proposed. A small number of probe particles are generated to roughly locate the target. The CNN is used to extract the image features, determine the target probability, and recognize the pattern of the target. A large number of searching particles are placed near the region where the target features are detected by the probe particles. The nearest neighbor clustering algorithm is used to classify the particles, which are recognized as the same category into different target sets. The positions of the targets can be determined by the bounding rectangle of the searching particles in the same target set. The method can be used to recognize and locate various kinds of targets. Furthermore, the method need not label the borders of the targets in the training samples, which enhance the generation efficiency of the samples. The simulation results show that the correctness of the recognition can be slightly improved, and the accuracy of the location can be significantly improved.
In order to improve the noise reduction performance and the clarity of denoising images, a composite convolutional neural network composed of the convolutional autoencoder network and the feature reconstruction network is proposed. Multiple convolutional layers are added into the autoencoder to extract the image feature information and improve the denoising performance, and the feature reconstruction network is designed to recover the texture and detail information of the image. The cross-connected structure is used to fuse feature information in the convolutional autoencoder network into the feature reconstruction network. Experimental results show that the proposed method has better noise reduction performance than the existing methods for different noise intensity. More texture and detail information could be retained, and the clearer denoising images could be obtained.
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