Obtaining information on parking slots is a prerequisite for the development of automatic parking systems, which is an essential part of the automatic driving processes. In this paper, we proposed a parking-slot-marking detection approach based on deep learning. The detection process involves the generation of mask of the marking-points by using the Mask R-CNN algorithm, extracting parking guidelines and parallel lines on the mask using the line segment detection (LSD) to determine the candidate parking slots. The experimental results show that the proposed method works well under the condition of complex illumination and around-view images from different sources, with a precision of 94.5% and a recall of 92.7%. The results also indicate that it can be applied to diverse slot types, including vertical, parallel and slanted slots, which is superior to previous methods.

The accuracy of automated parking technology that uses ultrasonic radar or camera vision for obstacles and parking space identification can easily be affected by the surrounding environment especially when the color of the obstacles is similar to the ground. Additionally, this type of system cannot recognize the size of the obstacles detected. This paper proposes a method to identify parking spaces and obstacles based on visual sensor and laser device recognition methods by installing a laser transmitter on the car. The laser transmitter produces a checkerboard-shaped laser grid (mesh), which varies with the condition encountered on the ground, which is then captured by the camera and taken as the region of interest for the necessary image processing. The experimental results show that this method can effectively identify obstacles as well as their size and parking spaces even when the obstacles and the background have a similar color compared to when only using sensors or cameras alone. recognize the size of objects, and binocular cameras are more expensive and the algorithms more complex [5][6][7][8].Another technology that APS uses for parking space recognition is the LiDAR sensing system. This technology was originally developed and used by the military. It detects the distance of the surrounding environment by launching a multi-beam pulsed laser rotating 360 degrees, and it can also draw a 3D map. However, as a result of the high cost of this technology, LiDAR with a low wire harness is used in vehicles, which comes with a decrease in resolution, making it susceptible to producing blind spots and resulting in safety concerns [9][10][11].The multi-sensor fusion technique for APS as proposed by many researchers is to overcome the above shortcomings of the other technologies. However, the high cost of this technology coupled with the fact that it has not sufficiently matured hinder its adoption [12][13][14]. Additionally, the theory of parking space recognition rarely considers the general situation of the obstacles in the parking space, which can lead to inaccurate obstacle identification [15].In order to solve the above challenges, a new parking space recognition scheme is proposed in this paper. A laser transmitter is added to the vision sensor system. A chessboard effect laser grid is presented on the ground by a laser emitter mounted on the vehicle, and the shape of the laser grid will change when there are obstacles on the ground. After these changes are captured by the camera, through image processing, the laser mesh region is taken as the region of interest. This method can effectively identify parking spaces and obstacles in parking spaces and significantly improve the recognition rate of sufficient parking spaces.