Due to the low contrast and color distortion of underwater images caused by the absorption and scattering of light by water, this paper proposes an underwater image enhancement strategy based on the attention mechanism of pyramids. Based on FUnIE-GAN, the feature extraction module uses MobileNet to replace the VGG16 model in the original U-Net structure, which reduces the number of network model parameters and accelerates the inference speed of the network model. Furthermore, the pyramid attention module is introduced into the generative network. The multi-scale pyramid feature and attention mechanism collection is beneficial to enhance the network feature extraction ability and improve the model performance. Experiments are carried out on EUVP dataset. The results show that the underwater images enhanced by our method are better in terms of subjective and objective aspects and have improved sharpness, color correction and contrast. The average values of peak signal-to-noise ratio and structural similarity were 21.398 and 0.742, respectively that is better than the other comparison methods.
Target detection is one of the hot research issues in the field of computer vision in recent years. Large scale convolution networks can effectively improve the accuracy of target detection, but they are not suitable for application scenarios with limited computing and storage capacity. In order to solve the above problems, this paper proposes a lightweight target detection network based on improved YOLOv5 - CS-YOLOv5 network. This network uses a lightweight network ShuffleNetv2 to reconstruct backbone, effectively reducing the network complexity, and adds a CA attention module to the feature extraction network to enhance the network feature expression ability, and solves the problem of neuron deactivation through H-Swish activation function. The final model size was compressed to 1.64MB, and the mAP50 on the COCO test set reached 47.6%, reducing the model size by 13.7% and improving the detection accuracy by 1.9% compared with the original YOLOv5 model.
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