# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
###### Abstract  
Lane detection, as a basic environmental perception task, plays a significant role in the safety of automatic driving. Modern lane detection methods have obtained a better performance in most scenarios, but many are unsatisfactory in various scenarios, with a weak appearance (e.g., serious vehicle occlusion, dark shadows, ambiguous markings, etc. ), and have issues in simplifying model predictions and flexibly detecting lanes of a non-fixed structure and number. In this work, we abstracted the lane lines as a series of discrete key points and proposed a lane detection method of parallel multi-scale feature aggregation based on key points, FPLane. The main task of FPLane is to focus on the precise location of key points in the global lanes and aggregate the global detection results into the local geometric modeling of lane lines by using the idea of association embedding. Furthermore, this work proposes the parallel Multi-scale Feature Aggregation network (MFANet) in FPLane integrating the context information of multi-scale feature mappings to take full advantage of the prior information of adjacent positions. In addition, MFANet incorporates the Double-headed Attention Feature Fusion Up-sampling module, which can facilitate the network to accurately recognize and detect objects under extreme scale variation. Finally, our method is tested on Tsimple and CULane lane detection datasets; the results show that the proposed method outperforms the current mainstream methods: the accuracy and F1-score of the model are 96.82% and 75.6%, respectively, and the real-time detection efficiency of the model can maintain 28 ms.  
lane detection; autonomous driving; deep learning +
Footnote †: journal: Medical Image Processing  
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Footnote 1: email: [EMAIL_ADDRESS]  
Footnote †: journal: Medical Image Processing  
such as a low detection accuracy and weak generalization ability of models under complex and changeable real-world scenarios. Deep-learning-based detection methods can achieve pixel-level detection with their powerful representation learning ability and massive data resources. Most lane detection methods [10; 11; 12; 13; 14; 15] mark lane lines and background pixel points into individual categories by training a convolutional neural network, and predict the position and shape of objects pixel by pixel. The network structure of these methods mainly adopt the high-to-low and low-to-high series codec framework. The high-to-low encoder first generates a low-resolution feature map that includes high-level semantic information; then, the low-to-high decoder is used to recover the feature map to its original resolution, and, finally the pixel-level predictions are output. When the lanes are severely degraded or completely occluded, the series structure, like these methods, often ignores the geometric priori and relevance between lanes themselves, and struggles to extract effective discriminant representations, yielding an inferior detection accuracy. In addition, some mark the location in the image where each row is most likely to contain part of the lane by a row-wise detection approach [16; 17], and others use an anchor-based detection approach to optimize the geometry of the lane curve [18; 19; 20]. These methods do not focus on the structure of the model but generate a large number of parameterized predictions, which increases the computation costs of the model. Usually, they also require the post-processing (usually a heuristic) to shape parameterized predictions into geometric curves, which further hinders the detection efficiency of the algorithm. To avoid post-processing, the curve parameter prediction strategy is investigated [21; 22]. They directly predict parametric equations to express the geometry of lanes, but the limitation is the inflexibility of equations, where only a fixed structure and fixed number of lanes can be detected.