Famsyolo network-based method and system for detecting surface defects of new vehicles

A Feature-adaptive multi-scale You Only Look Once (FamsYOLO) network-based method for detecting surface defects of new vehicles, comprising following steps:

• step 1:
• acquiring images of front, rear, left, right, and top surfaces of different types of new vehicles before delivery, marking vehicle surface defects in the images, and constructing an image dataset;
• step 2:
• constructing a FamsYOLO network,
• wherein the FamsYOLO network comprises three parts:
• a backbone part, a neck part, and an output part;
• the backbone part is serially formed by a head Darknetconv2d-BN-LeakyReLU (DBL)-3 module and an intermediate residual structure from top to bottom;
• the intermediate residual structure is serially formed by one one-layer residual module and three two-layer residual modules;
• the neck part comprises, from bottom to top in sequence, a DBL-1 module, upsampling, a first feature concatenation, a first FasterC3K2 module, Reshape transformation, a second feature concatenation, a second FasterC3K2 module, and a Squeeze-and-Excitation Network (SENet) module;
• an output of the third two-layer residual module of the backbone part is connected to the DBL-1 module of the neck part;
• an output of the DBL-1 module is subjected to upsampling processing and then feature-concatenated with an output of the second two-layer residual module of the backbone part, then processed by the first FasterC3K2 module, and further feature-concatenated with a result obtained by subjecting an output of the first two-layer residual module of the backbone part to the Reshape transformation in the neck part;
• after processing by the second FasterC3K2 module and the SENet module, an output of the neck part is obtained;
• the first FasterC3K2 module comprises a 1×1 convolution kernel I, a feature splitting operation, a first FasterC3 module, a second FasterC3 module, feature concatenation, and a 1×1 convolution kernel II that are connected in sequence;
• a part of results of the feature splitting operation is sequentially input to the first FasterC3 module and the second FasterC3 module for processing, and another part of the results of the feature splitting operation is feature-concatenated with an output of the first FasterC3 module and an output of the second FasterC3 module, and then processed by the 1×1 convolution kernel II to obtain an output of the first FasterC3K2 module;
• the first FasterC3 module and the second FasterC3 module each comprise a 1×1 convolution kernel III, a FasterBlock module, feature concatenation, and a 1×1 convolution kernel IV that are connected in sequence;
• an output of the 1×1 convolution kernel III is feature-concatenated with an output of the FasterBlock module, and processed by the 1×1 convolution kernel IV to obtain the output of the first FasterC3 module and the output of the second FasterC3 module;
• the FasterBlock module comprises a partial 3×3 convolution kernel, a 1×1 convolution kernel V, batch normalization (BN), a Rectified Linear Unit (ReLU) activation function, and a 1×1 convolution kernel VI that are connected in sequence;
• an input of the partial 3×3 convolution kernel is residually connected with an output of the 1×1 convolution kernel VI to obtain an output of the FasterBlock module;
• the output part comprises, from top to bottom in sequence, a first DBL-1 module, a first DBL-3 module, a second DBL-1 module, a second DBL-3 module, and a 1×1 convolution kernel VII;
• by processing an output feature map of the neck part, the output part is capable of recognizing vehicle surface defects in an image and displaying a category and a bounding box of a detected target in the image;
• and
• step 3:
• training the FamsYOLO network using the image dataset from the step 1, wherein the trained FamsYOLO network is used for detecting surface defects of new vehicles.