The invention provides a campus road crack detection method based on a Mobilenet-PSPNet neural network model, and the method comprises the following steps: S1, collecting a crack image data set, manually calibrating a label, and converting the label into a corresponding mask bitmap; S2, designing a MobileNet-PSPNet neural network, and training the MobileNet-PSPNet neural network by using the image processed in the step S1; S3, collecting campus road surface images and transmitting the campus road surface images to the terminal in real time; S4, extracting global features of the data set image acquired in the step S3 by using MobileNet; S5, extracting local features of the global feature map obtained in the step S4 through a pyramid adaptive average pooling module in the MobileNet-PSPNet network; S6, performing up-sampling operation on the local features obtained in the step S5, and then performing feature fusion on the local features and the global features to obtain new features including the global features and the local features; and S7, obtaining a final prediction result through convolution and up-sampling operation. According to the method, the crack of the campus road is detected in real time through the improved Mobilenet-PSPNet based on the PSPNet convolutional neural network, the method is accurate and efficient, and wrong detection and missing detection are not easy to generate.