To address the challenge of low detection accuracy of small targets, such as cyclists and pedestrians in the PointPillars algorithm, an improved PointPillars algorithm based on point cloud feature enhancement is proposed. First, the quality of the input point cloud data is improved by increasing the environmental density aware sampling and IFPS point cloud sparsity. Second, a stepped ECA attention mechanism is integrated into the point cloud feature encoding to enhance the point cloud features through multilevel attention guidance, and a feature fusion enhancement module is added to the backbone network to strengthen the interaction between feature maps at different levels. Finally, the introduction of the EMA attention mechanism further enhances the point cloud features in the feature map. The experimental results based on the KITTI dataset indicate that the proposed improved algorithm improves the three-dimensional average detection accuracy of pedestrians and cyclists in simple, moderate, and difficult scenarios by 7.9 percentage points, 8.6 percentage points, 8.3 percentage points, and 4.0 percentage points, 2.9 percentage points, 3.8 percentage points, respectively, compared to the original algorithm. Furthermore, the average directional similarity increases by 7.5 percentage points, 7.9 percentage points, 5.9 percentage points, and 4.4 percentage points, 5.2 percentage points, 6.1 percentage points, respectively.