Lane line detection method, vehicle-mounted device and storage medium

A vehicle-mounted device comprising:

• a storage device;
• at least one processor;
• and
• the storage device storing one or more programs, which when executed by the at least one processor, cause the at least one processor to:
• obtain a foreground image of a vehicle;
• convert the foreground image into a bird's eye view (BEV) image;
• establish a horizontal histogram image corresponding to the BEV image;
• set a peak of the horizontal histogram as a starting point while moving a horizontal sliding window, and generate a main lane line by fitting a plurality of non-zero pixels of the horizontal sliding window;
• calculate a confidence coefficient of each horizontal sliding window;
• in response that a plurality of confidence coefficients of a preset number of continuous horizontal sliding windows are less than a predetermined threshold, set a sliding window that is previous before the preset number of continuous horizontal sliding windows as a previous horizontal sliding window, and determine an end of the main lane line according to the previous horizontal sliding window;
• and
• establish a vertical histogram according to the BEV image, and set a peak of the vertical histogram as a starting point while moving a vertical sliding window;
• generate a target curve by fitting the non-zero pixels of the vertical sliding window;
• and
• determine that the target curve is a stop line in response that the end point of the lane line being on the target curve.

A non-transitory storage medium having instructions stored thereon, when the instructions are executed by a processor of a vehicle-mounted device, the processor is caused to perform a lane line detection method, wherein the method comprises:

• obtaining a foreground image of a vehicle;
• converting the foreground image into a bird's eye view (BEV) image;
• establishing a horizontal histogram image corresponding to the BEV image;
• setting a peak of the horizontal histogram as a starting point while moving a horizontal sliding window, and generating a main lane line by fitting a plurality of non-zero pixels of the horizontal sliding window;
• calculating a confidence coefficient of each horizontal sliding window;
• in response that a plurality of confidence coefficients of a preset number of continuous horizontal sliding windows are less than a predetermined threshold, setting a sliding window that is previous before the preset number of continuous horizontal sliding windows as a previous horizontal sliding window, and determining an end of the main lane line according to the previous horizontal sliding window;
• and
• establishing a vertical histogram according to the BEV image, and setting a peak of the vertical histogram as a starting point while moving a vertical sliding window;
• generating a target curve by fitting the non-zero pixels of the vertical sliding window;
• and
• determining that the target curve is a stop line in response that the end point of the lane line being on the target curve.