The activation functions play increasingly important roles in deep convolutional neural networks. The traditional activation functions have some problems such as gradient disappearance, neuron death and output offset, and so on. To solve these problems, we propose a new activation function in this paper, Fast Exponentially Linear Unit (FELU), aiming to speed up exponential linear calculations and reduce the time of network running. FELU has the advantages of Rectified Linear Unit (RELU) and Exponential Linear Unit (ELU), leading to have better classification accuracy and faster calculation speed. We test five traditional activation functions such as ReLU, ELU, SLU, MPELU, TReLU, and our new activation function on the cifar10, cifar100 and GTSRB data sets. Experiments show that the proposed activation function FELU not only improves the speed of the exponential calculation, reducing the time of convolutional neural network running, but also effectively enhances the noise robustness of network to improve the accuracy of classification.
The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity.
Recently, many excellent algorithms have made great progress in object detection, but there are also problems in these algorithms’ performance on targets of different sizes, and in particular in small object detection. Aiming at the problem of insufficient feature representation by the feature extractor, in this paper we propose a lightweight algorithm to improve feature extraction. The algorithm includes three modules. First, considering that the shallow features in feature extraction contain much background noise, in this paper we design a multi-level feedback propagation model based on a Gaussian high-pass filter. The shallow layers are enhanced using the filter and then back-propagated to add the upper shallow layer features and obtain new shallow layer features. This process is performed on the newly generated shallow layer for n iterations, which is beneficial for enhancing targets in the foreground area and suppressing background noise. Second, we form a stacked dilated convolution module with different dilation rates to cover the entire deep feature layer densely, which enlarges the receptive field and enriches the contextual information. Finally, we build a multi-scale fusion module to fuse the above-mentioned enhanced shallow and deep features to obtain output features with powerful representational ability for detection tasks. In addition, the model is easily embedded into existing approaches to enhance their performance. We build the model on the VGG-16 and ResNet-50 backbones and successfully applied it on Darknet-19 and Darknet-53 to verify its effectiveness and stability. The experiments on the COCO dataset prove that the proposed algorithm outperforms the state-of-art methods, with a mean average precision improvement reaching 2% on average. The effect is remarkable on small targets and complex backgrounds. Furthermore, it does not affect the detection speed significantly, so real time detection requirements can still be met.
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