An efficient vision-based parking slot detection method

Abstract High-accuracy detection of vacant parking slots using visual system is a prerequisite for automatic parking, which is significant for the promotion of automatic parking. Complex visual environment such as light occlusion, intensity variations and visual accuracy raises significant challenges for vision-based system parking slot detection. To address such problems, REPSDet (Resnet50 based Parking Slot Detection) is proposed for parking slot detection and occupancy classification in this paper, which is based on deep learning. Parking slot proximity information could be fully extracted by REPSDet,which is combined with parking slot classification and location information to improve the parking slot detection effect. For parking slot detection, REPS-Net is proposed in this paper, which is an improved yolov4 parking slot detection method. In REPS-Net, marker point positioning combined with geometric clues are used to infer the precise location of parking slots. For occupancy classification, the deep convolutional network m\_DCNN is proposed in this paper. Network structure and operation performance are optimized by m_DCNN, and the accuracy of parking slot detection is further improved. Experimental results show that REPSDet achieves precision rate of 97.77% and recall rate of 99.38% on the PS2.0_A dataset, which is significantly better than compared methods.