The invention relates to a channel
foreign matter image generation and channel
foreign matter detection method based on a
generative adversarial network, and the method comprises the steps: image collection: employing an unmanned plane for inspection, and collecting a channel image;
image noise reduction: carrying out
noise reduction
processing on the acquired image by adopting a median filtering technology; model improvement: based on the GAN model, introducing an SE attention mechanism, and improving a
loss function to obtain an improved GAN model; model training: training the improved GAN model through a training
data set;
image generation: generating a channel
foreign matter image by using the trained foreign matter
image generation model, and forming an extended sample set; foreign matter recognition: training a target detection network in combination with the original sample set and the extended sample set, and detecting and recognizing the channel foreign matter based on the trained target detection network; and foreign matter positioning: according to an identification result, based on a GIS
system, determining a spatial position of a foreign matter. The method is beneficial to generating images similar to real channel foreign matter features, and the channel foreign matter detection precision is improved.