# Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
They used the RANSAC algorithm for guide line detection to ensure that the guide line of a parking space can be effectively identified in noisy environments. The limitation of this method is that it is only suitable for vertical parking spaces and requires clearly visible guidance lines. Different from the straight-line based berth detection methods, there are other corner-based berth detection methods, with the work of [PERSON] and [PERSON] being representative. Refs. [14; 15] They used the Harris corner detector [16] to detect significant corner spots in parking spaces. The Harris corner detector is a classical corner detection algorithm. By calculating the local structure matrix of the image, it identifies the image regions with significant changes, which are usually corners or boundary intersections. After the corner detection is completed, [PERSON] and [PERSON] combine these corners to form the so-called nodes. Based on these nodes and geometric clues in the parking lot, the complete parking space contour is deduced by inference. With the development of deep learning in recent years, input images are put into specific neural networks, and through continuous iterative learning mechanisms, weak classifiers are gradually trained into strong classifiers, so that they can obtain more accurate results for specific tasks. Deep convolutional neural networks (DCNNs) have emerged as a fundamental architecture within neural networks, widely applied in domains such as computer vision, image recognition, object detection, and natural language processing. Unlike traditional image processing methods that rely on manually designed feature extraction techniques, DCNNs automatically learn essential features directly from images via a multi-layer network. Typically, DCNNs are composed of three key types of layers: convolutional layers, pooling layers, and fully connected layers. Convolutional layers are responsible for detecting low-level features, such as edges and textures, which are progressively integrated to form complex patterns. Pooling layers reduce the dimensionality of the data, enhancing computational efficiency. Finally, fully connected layers consolidate the extracted features to generate predictions or classification outputs. The development of DCNN can be traced back to the 1980s, but the real breakthrough came in 2012, when AlexNet [17] made a significant achievement in the ImageNet competition, greatly advancing the research of deep learning and convolutional neural networks. Since then, many advanced DCNN architectures have been introduced, such as VGG [18; 19; 20; 21], ResNet [22; 23; 24], DenseNet [25; 26; 27], etc. DCNN can automatically learn and extract useful features from the original data, reduce the dependence on manual feature engineering, greatly reduce the number of parameters in the model through parameter sharing in the spatial dimension of the convolution kernel, and improve computational efficiency. The method based on DCNN has made great contributions in many fields, among which the object detection field discussed in this paper also benefits from this. The application of DCNN in target detection tasks began with the groundbreaking R-CNN study by [PERSON] et al. [28] R-CNN is actually a multi-stage detection framework. First, for the input image, the target proposal algorithm is used to generate a candidate bounding box containing a high probability target. Then, the standard DCNN is used as a feature extractor to extract the feature of each candidate bounding box, and finally, the category of the target in the bounding box is determined by the classifier. On the basis of the R-CNN framework, many researchers have proposed a variety of improvement methods to improve its performance, among which the representative ones include Fast R-CNN [29], Faster R-CNN [30], Mask R-CNN [31], etc.