Fig. 1. Flow chart of parking space recognition

Fig. 2. Partial images of car models. (a) Side view; (b) top view

Fig. 3. Simulated result of total loss value

Fig. 4. Recognition effects of training models on car models. (a) Identification of partial verification assessments; (b)(c) test object recognition model; (d) results by filtering and recognition

Fig. 5. Distribution of β in BN processing of one layer in depthwise separable convolution kernel

Fig. 6. Recognition when simulating full parking space

Fig. 7. Visualization after sorting and numbering of parking space data. (a) Before using data layering method; (b) after using data layering method

Fig. 8. Frame image about parking space occupancy

Fig. 9. Visualization of car identification data

Fig. 10. Probability discriminant model for empty parking spaces. (a) Schematic for discriminating empty parking space; (b) flow chart

Fig. 11. Output of parking space detection results

Fig. 12. Edge detection of cars in different environments by Canny operator. (a) Edge detection result of Fig. 4(a); (b) edge detection result of Fig. 6

Fig. 13. Model training and verification in case verification. (a) Change in total loss value; (b) recognition effect after iterative training for 25000 times

Fig. 14. Parking space recognition. (a) Frame image of full parking space; (b) visualization of identification data for parking spaces

Fig. 15. Detection of empty parking spaces at some time point. (a) Parking space occupancy; (b) recognized car coverage of parking spaces; (c) output of parking space detection results

Fig. 16. Parking space identification. (a) Frame image of full parking space; (b) visualization of identification data for parking spaces

Fig. 17. Detection of empty parking spaces at some time point. (a) Parking space occupancy; (b) recognized car coverage of parking spaces; (c) output of parking space detection results