The invention discloses a
tidal bore tide head line detection and segmentation method. According to the method, aiming at the problems of low tide line detection precision, weak generalization ability and large calculation overhead of an existing method, an instance segmentation method is innovatively used for detection. The method specifically comprises the following steps: introducing a C3k2ODConv module into a neck network, constructing a four-dimensional collaborative attention mechanism by using full-dimensional dynamic
convolution, and enhancing the extraction and expression ability of a model to multi-scale features; a C2PSA module in a
backbone network is replaced by a C2PSADA T module, and a DA attention mechanism and a feedforward
network structure are introduced, so that the sensing and positioning capability of a key area in an image is improved; and meanwhile, a
backbone network is replaced by a MobileNetV4 architecture, and a Fused-IB module and a UIB module are combined, so that the balance of model lightweight and deep semantic modeling is realized. Experiments prove that under the IoU threshold value of 50-95%, the average
mask precision of the method reaches 68.2%, the
model parameter quantity is 6.513 M, the reasoning speed is 25.2 ms / frame, the method is remarkably superior to an original model, detection precision and deployment efficiency are both considered, and the method is suitable for intelligent
tidal bore monitoring in the complex environment.