Unmanned parking space detection method based on panoramic surround view

An unmanned parking space detection method based on a panoramic surround view, comprising:

• a step 1:
• respectively acquiring a front original image, a rear original image, a left original image, and a right original image of a surrounding environment of an automobile through a front-view camera, a rear-view camera, a left-view camera and a right-view camera arranged on the automobile;
• a step 2:
• respectively performing distortion removal processing and perspective transformation processing on the front original image, the rear original image, the left original image, and the right original image to obtain a front view, a rear view, a left view, and a right view;
• a step 3:
• extracting feature points from the front view, the rear view, the left view and the right view by a random sample consensus (RANSAC) algorithm, and determining areas to be spliced and fused;
• a step 4:
• fusing the areas to be spliced and fused by a Laplace fusion algorithm to obtain the panoramic surround view, and adjusting a quantity of layers of a Laplace pyramid according to an image quality evaluation function in a fusion process;
• and
• a step 5:
• inputting the panoramic surround view into a trained parking space detection model based on deep learning to obtain a parking space attribute result and a parking space identification point result;
• wherein the trained parking space detection model based on deep learning uses a repeated visual geometry group network (REP-VGG) network as a network trunk, and after the network trunk is output, the network trunk passes through a Sim-CSPSPPF module and residual convolution modules, branches of the network trunk pass through the residual convolution modules, and different parking space results are output on feature maps with different resolutions;
• the parking space attribute result is predicted by a first feature map with a lower resolution, and the parking space identification point result is predicted by a second feature map with a higher resolution.