Image Water Ripple Detection Method Based on Constraint Convolution and Attention Mechanism

Abstract

Noise can introduce irrelevant interference signals, reduce the signal-to-noise ratio of the image, weaken the contrast between the target and the background, and make it more difficult to detect the target in the image, thus increasing the difficulty of water ripple detection. Therefore, a method for image water ripple detection based on constraint convolution and attention mechanism is proposed. Using the attention mechanism for image denoising, the “attention map” is calculated from both channel and spatial aspects, and the calculated “attention map” is multiplied by the image feature map for adaptive feature learning to achieve image denoising processing. The convolutional neural network is used to extract the features of the input image. Based on feature extraction, the constrained convolution operation is applied to highlight the detailed features of water ripples. The features obtained from the constrained convolution operation are input into the support vector machine classifier for the classification and detection of water ripples. According to the relationship between the patterns and features learned by the classifier, whether the image belongs to the category of water ripples is judged, to achieve water ripple detection. The experimental results show that the proposed method has a good image denoising effect and water ripple detection effect.