The parking space enhancement algorithm is an important part of automatic parking, and its enhancement result directly affects the extraction effect of the parking space line. Based on this, this paper introduces the dark channel as a low-frequency component for adaptive contrast enhancement. Based on multiple sets of low-contrast parking space image data, the applicability of various low-contrast enhancement algorithms is discussed. The exposure phenomenon leads to the problem of reducing the integrity and accuracy of parking space line extraction. A fast enhancement algorithm for peak histogram equalization is proposed, which combines PSNR, structural similarity, average brightness and information entropy as objective evaluation indicators, and uses Hough The linear detection statistical algorithm enhances the accuracy of the parking space extraction results and is verified. The research results show that the algorithm in this paper can reduce the interference of environmental information, retain more texture details, improve the brightness and contrast of the global image, and it still has excellent robustness in low-light environments. The algorithm in this paper has a parking space line extraction accuracy of more than 90%, and the algorithm running time is only 37.18 ms, which can provide method guidance for automatic parking systems in low-contrast scenes.