Object detection is the central issue of intelligent traffic systems, and
recent advancements in single-vehicle lidar-based 3D detection indicate that it
can provide accurate position information for intelligent agents to make
decisions and plan. Compared with single-vehicle perception, multi-view
vehicle-road cooperation perception has fundamental advantages, such as the
elimination of blind spots and a broader range of perception, and has become a
research hotspot. However, the current perception of cooperation focuses on
improving the complexity of fusion while ignoring the fundamental problems
caused by the absence of single-view outlines. We propose a multi-view
vehicle-road cooperation perception system, vehicle-to-everything cooperative
perception (V2X-AHD), in order to enhance the identification capability,
particularly for predicting the vehicle's shape. At first, we propose an
asymmetric heterogeneous distillation network fed with different training data
to improve the accuracy of contour recognition, with multi-view teacher
features transferring to single-view student features. While the point cloud
data are sparse, we propose Spara Pillar, a spare convolutional-based plug-in
feature extraction backbone, to reduce the number of parameters and improve and
enhance feature extraction capabilities. Moreover, we leverage the multi-head
self-attention (MSA) to fuse the single-view feature, and the lightweight
design makes the fusion feature a smooth expression. The results of applying
our algorithm to the massive open dataset V2Xset demonstrate that our method
achieves the state-of-the-art result. The V2X-AHD can effectively improve the
accuracy of 3D object detection and reduce the number of network parameters,
according to this study, which serves as a benchmark for cooperative
perception. The code for this article is available at
this https URL

通过提出一种多视角车路协同感知系统（V2X-AHD），利用不同的训练数据提高轮廓识别的准确性，并借助多头自注意力（MSA）融合单视角特征，从而有效提高 3D 物体检测的准确性和降低网络参数数量。