# Grid Anchor Lane Detection Based on Attribute Correlation
## 2 Related Work  
Lane detection involves extracting features from images to perform instance segmentation [4, 22, 23], with the output represented as either coordinate point sequences or curve equations for lane lines. Traditional detection methods [24, 25, 26, 27, 28] primarily rely on edge features of images. However, due to the superior robustness of deep learning techniques, detection methods based on deep learning have become the mainstream approach. Deep learning-based methods for lane detection can be categorized into four types: segmentation-based methods, keypoint-based methods, parameter-based methods, and anchor-based methods. Segmentation-based methods. Inspired by semantic segmentation, researchers classify each pixel in an image as either background or lane lines to achieve precise alignment with the shape of the lane lines. LaneNet [12] adopts an instance segmentation approach to detect a variable number of lane lines, while simultaneously outputting learned inverse perspective transformation parameters to improve lane line fitting. SCNN [13] and RESA [14] leverage both horizontal and vertical convolutions to capture richer spatial information. Among these, RESA demonstrates higher computational efficiency due to its superior parallelism. ENet-SAD [11] employs Self-Attention Distillation (SAD) to better capture detailed features of high-level and low-level layers. UNet-ConvLSTM [15] combines CNN and RNN frameworks, utilizing temporal information from multiple images to further enhance detection accuracy. However, this category of methods involves significant computational overhead, making slow processing speed their primary limitation. Keypoint-based methods. Some studies regard lane detection as a task of keypoint localization and confidence prediction. PINet [29] utilizes a stacked hourglass network to learn features at multiple scales of feature maps, outputs the confidence and offsets of keypoints, and adopts an instance segmentation approach to cluster the keypoints. FOLOLane [30] selects high-confidence points as keypoints at evenly spaced vertical positions in an image, predicts the horizontal offsets of adjacent keypoints, and reconstructs lane lines through geometric decoding. GANet [31] generates a confidence map for keypoints and an offset map relative to the starting points of lane lines through convolutional computations. Unlike other methods, GANet performs instance segmentation by clustering lane line starting points. However, keypoint-based methods heavily depend on low-level image features, leading to poor robustness in scenarios involving occlusions or environmental interference. Parameter-based methods. Parameter-based methods model lane lines as piecewise spline curves and output polynomial coefficients by fitting lane line features. E2 EDLSF [19] employs an end-to-end approach to regress solutions for lane lines fitted by the least squares method. PolyLaneNet [18] uses deep learning to directly output a fixed number of polynomial coefficients and the corresponding confidence scores for each polynomial. BezierLaneNet [16] horizontally flips high-level feature maps for symmetry and predicts the parameters of Bezier curves to determine the shape and position of lane lines. E2 ET [17] extracts features using ResNet [32] and employs Transformers [33] to regress cubic polynomial parameters for lane lines. These methods transform the traditional two-step process of feature extraction and post-processing into a streamlined single-step process with direct output from the neural network. However, due to the diverse shapes of lane lines, fitting them with predefined parametric curves introduces inherent biases and lacks flexibility. As a result, these methods exhibit relatively low detection accuracy and require further improvement. Anchor-based methods. Anchor-based detection methods first calculate the confidence scores and the relative coordinates of lane lines with respect to anchors using a neural network.