An Inspection Method of Urban Road Parking Based on UAV Image

Efficient and accurate inspection of parked vehicles on urban roads is of significant importance for smart city management. Addressing the issues of low efficiency, high cost, and inaccuracy in current inspection methods, a drone-based image inspection approach is investigated. To enable all-weather inspection, an illumination enhance-ment algorithm is employed to boost images captured under low-light conditions, while a deblurring algorithm is uti-lized to improve the quality of blurred images. To tackle the limitations of the existing YOLOv5 algorithm, includ-ing insufficient detection accuracy and real-time performance, several modifications are introduced. The Fo-cal-EIOU Loss function is optimized to accelerate model convergence. The C3 module is replaced with the C2F module, utilizing varying sizes of convolutional kernels to extract features, enhancing adaptability to targets of dif-ferent sizes and shapes. Furthermore, the SimAM attention mechanism is incorporated to improve the network's ro-bustness and anti-interference capability, predicting 3D attention weights for feature maps without increasing model parameters. The CARAFE operator is adopted for upsampling to expand the receptive field, comprehensively lever-aging semantic information from feature maps. Experimental results demonstrate that the modified YOLOv5 model achieves 5.1% increase in accuracy, 5.9% improvement in recall, and 3.6% enhancement in mean Average Precision (mAP). Secondly, the SVTR character recognition network is utilized to identify license plate numbers, accomplish-ing both feature extraction and text transcription tasks within a single vision model. Finally, field tests conducted on a drone-based engineering application platform reveal that this inspection method can accurately, rapidly, and intelli-gently complete inspections, achieving an accuracy of 90% and a detection speed of 170 frames per second, essentially meeting inspection precision and real-time requirements.