Yunxiao Shan

Autonomous driving is a prominent research area, with the primary challenge being the precise perception and interpretation of the surrounding environment. To address issues such as low resolution, dense occlusion, and small targets in complex road scenes, we propose an improved road target detection algorithm, SNCE-YOLO, based on YOLOv5. Firstly, to avoid the loss of feature information during training for low-resolution and small targets, the Space-to-Depth Convolution(SPD-Conv) is introduced to replace the original convolutional layer for retaining global feature information. Secondly, to address the issue of confusing complex backgrounds with detection targets when features are extracted too early and deeply, the Normalization-based Attention Module (NAM) is incorporated into the backbone network without increasing the computational effort, focusing on the channel and spatial information of interest. Thirdly, to enhance the capture and fusion of multi-level feature information, we optimized the neck network’s feature fusion module by implementing the C2f structure. Finally, to reduce the bias in the regression of the occluded target positions, we introduced the EIOU loss function to better define the relationship between the predicted and real frames. The SNCE-YOLO algorithm achieves a mean Average Precision (mAP@0.5) of 91.9% on the KITTI dataset, which represents a 2.6% improvement over the baseline model. Additionally, with a detection speed of 92.54 frames per second (FPS), it meets the requirements for real-time target detection in complex road scenes. Comparisons are also made with other mainstream target detection algorithms to verify the effectiveness of SNCE-YOLO.

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Density peaks clustering has become a nova of clustering algorithm because of its simplicity and practicality. However, there is one main drawback: it is time-consuming due to its high computational complexity. Herein, a density peaks clustering algorithm with sparse search and K-d tree is developed to solve this problem. Firstly, a sparse distance matrix is calculated by using K-d tree to replace the original full rank distance matrix, so as to accelerate the calculation of local density. Secondly, a sparse search strategy is proposed to accelerate the computation of relative-separation with the intersection between the set of $k$ nearest neighbors and the set consisting of the data points with larger local density for any data point. Furthermore, a second-order difference method for decision values is adopted to determine the cluster centers adaptively. Finally, experiments are carried out on datasets with different distribution characteristics, by comparing with other six state-of-the-art clustering algorithms. It is proved that the algorithm can effectively reduce the computational complexity of the original DPC from $O(n^{2}K)$ to $O(n(n^{1-1/K}+k))$ . Especially for larger datasets, the efficiency is elevated more remarkably. Moreover, the clustering accuracy is also improved to a certain extent. Therefore, it can be concluded that the overall performance of the newly proposed algorithm is excellent.