Indoor Parking Space Detection Based on Improved YOLOv5m
Aiming at the situation that the existing detection algorithm has insufficient detection accuracy and low detection efficiency of target parking space under the indoor parking lot scene,a small target detection layer is added to the existing YOLOv5m to enhance the detection of small target samples,and a coordinate attention mechanism is introduced on this basis to reduce redundant information input and improve de-tection accuracy.At the same time,a large-scale indoor parking lot labeling dataset containing 8 100 underground parking space images is es-tablished,and experiments are carried out on this dataset,the mean average precision(mAP)of the method is 98.214%,the accuracy rate is 97.254%,and the recall rate is 96.548%,the results show that the algorithm greatly improves the accuracy of the model,the performance of parking space detection and the real-time detection of the model,and is feasible in the detection of parking spaces in indoor parking lots.
automated valet parkingtarget detectionparking space detectionend-to-end deep learningmonocular camera
万方数据
维普
