Patent ID: 11948344
Assignee: WUHAN UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 2:
3. The method for identifying and ranging inland vessels as claimed in claim 1, characterized in that the building of a MobilenetV1-YOLOv4 vessel identification model in Step 3 includes:
building a vessel target identification model based on the YOLOv4 model, with a network structure consisting of Backbone, Neck and Head; the YOLOv4 network uses CSP Dark net53 as the backbone network for feature extraction, combined with Cross Stage Partial (CSP) and residual network; the Neck module consists of a spatial pyramid pooling and a path aggregation network; the spatial pyramid pooling consists of three different scale pooling layers: 5×5 9×9 and 13×13; the path aggregation network serves as a feature fusion module, adding bottom-up paths on top of the feature pyramid network; the Head module is used to detect targets, and output feature maps of three different sizes: 13×13, 26, 26×26 and 52×52;
if the size of an input image is DK×DK, an input channel is M, the number of convolutional kernels is N, and a convolutional kernel size is DF×DF then an output feature map size is DC×DC and an output channel is N;
in traditional convolutional operations, number of parameters that need to be calculated is: Params1=DF×DF×M×N;
the calculation of the parameters for a depth-wise separable convolution is divided into two parts: one is a depth convolution parameter, and the other is a point convolution parameter, where the size of a point convolution kernel is 1×1; therefore, the parameters to be calculated are: Params2=DF×DF×M+M×N;
a ratio of parameters between the depth-wise separable convolution and standard convolution methods is:, P
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the MobileNetv1 is based on the depth-wise separable convolution module, which uses a 3×3 convolution kernel to perform depth convolution and extract feature information; a BN layer and a ReLU layer are connected between the depth convolution and the point convolution, and feature information is output through the BN layer and the ReLU layer after point convolution; the MobileNetv1 has a total of 28 layers; the first layer is a standard 3×3 convolution, with 13 depth-wise separable convolution modules built; before the fully connected layer, an average pooling layer is used to reduce the spatial resolution to 1, and the softmax layer is used to output probability for each category to be identified;
replace the backbone network CSPDarknet53 of YOLOv4 with MobileNetv1, connect a three different scale feature layers extracted by MobileNetv1 with the spatial pyramid pooling and path aggregation network module of the traditional YOLOv4 model, and build the Mobilenetv1-YOLOv4 target detection model.