To solve the problem of low detection precision of weakly perceived objects with missing shapes in distant or occluded scenes， a Weakly Perceived object detection method based on point cloud Completion and Multi-resolution Transformer （WP-CMT） was proposed. Firstly， since that some key information was lost due to the down-sampling convolution operation in object detection network， the Part-Aware and Aggregation （Part-A²） method with deconvolution up-sampling structure was chosen as the basic network to generate the initial proposals. Then， in order to enhance the shape and position features of the weakly perceived objects in the initial proposals， the point cloud completion module was applied to reconstruct the dense point sets on the surface of the weakly perceptive objects， and a novel multi-resolution Transformer feature encoding module was constructed to aggregate the completed shape features with original spatial location information of the weakly perceived objects， and then the enhanced local features of the weakly perceived objects were captured by encoding the contextual semantic correlation of the aggregated features on local coordinate point sets with different resolutions. Finally， the refined bounding boxes were generated. Experimental results show that WP-CMT achieves 2.51 percentage points gain on average precision and 1.59 percentage points on mean average precision compared to baseline method Part-A² for the weakly perceived objects at hard level in KITTI and Waymo datasets， which proves the effectiveness of the proposed method for weakly perceived object detection. Meanwhile， ablation experimental results show that the point cloud completion and multi-resolution Transformer feature encoding modules in WP-CMT can effectively improve the detection performance of weakly perceived objects for different Region Proposal Network （RPN） structures.

Poor generalization of feature parameters classification and large category computation reduce the classification performace of K-Nearest Neighbor (KNN). An improved KNN based on union entropy under the attribute reduction condition was proposed. Firstly, the size of classification impact of data feature was measured by calculating the union entropy of two feature parameters relative to any two condition attributes, and the intrinsic relation was established between classified features and the specific classification process. Then, the method which reduced condition attributes according feature union entropy set was given. The theoretical analysis and the simulation experiment show that compared with the classical KNN, the improved algorithm has better classification performance.