# Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
## 3 Result Analysis
### Overall Performance Analysis of Different Algorithms  
The reason why the YoloV11 algorithm can be outstanding in performance is that after many rounds of iterations, the whole algorithm, from YoloV1 in 2015 to YoloV11 in 2024, has been adjusted in the network structure for many times, which makes YoloV11 exhibit stronger feature extraction abilities and more efficient use of parameters compared to previous versions. The accuracy is improved, while the number of parameters is reduced, and the NMS is replaced with a more efficient algorithm. In target detection tasks, DETR algorithm is a true end-to-end algorithm, it does not need to set a prior box, and the final bounding box is completely generated by the network. The reason why the DETR algorithm can be so efficient is that the added Transform can enlarge the receptive field of the image to contain the whole image. The accuracy of the algorithm is improved by the self-attention mechanism, but the localization accuracy of the target frame is reduced at the same time. Therefore, considering all advantages and disadvantages, this paper believes that YoloV11 is the best choice to detect the key points of parking spaces. In the process of handling the global image and deducing the parking frame, we compared four methods, including the method proposed in this paper. It can be clearly seen in Table 5 that the accuracy rate of parking space inference can be above 98%, regardless of the method based on it. The idea proposed in this paper is inherited from the key point detector based on Yolo algorithm. The PSD_L decision tree-based marker detector is abandoned. However, the relative novelty is mainly reflected in two aspects. First of all, we use the latest YoloV11 as the key point detector instead of the used Yolov2. Secondly, in processing local images, we use the method based on deep learning to judge the category of parking space and propose a brand new method. Building a custom convolution kernel can also achieve similar effects. However, in terms of computational amount, it is necessary to obtain the category of parking space after five convolutional operations using a 3 \(\times\) 3 convolution kernel, three maximum pooling operations using a 3 \(\times\) 3 pooling kernel size, and two fully connected layers with 4096 neurons. By contrast, the adopted method only requires one convolutional operation with a 4 \(\times\) 3 convolution kernel to obtain the correct result. In this paper, the final size of the binary image is 56 \(\times\) 56, so in the example given in this paper, the judgment of the rotation of each parking space key point only needs 3136 times of multiplication operations and 3135 times of addition operations, which is one of the reasons why it can be ahead of other algorithms in computing power. The table shows the time and accuracy required from obtaining the global image to inferring the parking space. It can be seen from the table that the accuracy of various methods reaches more than 98%, but there is a huge difference in time consumption. The \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Time Cost (ms)** \\ \hline Faster-RCNN & 63.7 \\ SSD & 27.1 \\ DETR & 106.4 \\ YoloV11 & 9.3 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of calculation efficiency. \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Precision Rate** & **Time Cost (ms)** \\ \hline PSD\_L & 98.55\% & 269.4 \\ PSD\_L\_LIP & 98.19\% & 138.1 \\ DeepPS & 99.54\% & 95.6 \\ SecondStagePS & 98.24\% & 12.3 \\ \hline \end{tabular}
\end{table} 
Table 5: A comparison of the time and accuracy required to deduce the parking space. reason is that the method used in this paper does not continue to use a neural network to assist in inferring the parking space after obtaining the local image, as other algorithms do. Instead, a convolutional kernel is designed to replace the second convolutional neural network, which greatly reduces the computational time required.