Abstract
Traditional target detection methods based on manual features have limitations, and it is difficult to adapt to complex scenes and various types of targets. Recently, three-dimensional target detection tasks have achieved good performance using artificial intelligence technology that is based on deep learning methods. Based on this, this paper presents a three-dimensional laser point cloud target detection algorithm based on deep learning. First, the point cloud segmentation network is designed for data semantic segmentation of the original point cloud, and the point cloud is categorized in semantic regions. Then, a point cloud region proposal network to generate region proposals according to the categorization results. Finally, the target recognition network of the point cloud is designed to achieve the detection of three-dimensional objects, specifically by conducting proposal classification and position regression. The entire algorithm integrates key modules such as point cloud segmentation, area proposal, and target recognition end-to-end, and fully excavates the geometric characteristics and semantic information of point cloud data. Experimental verification is carried out on the public benchmark data set, and the results show that the proposed algorithm has achieved state-of-the-art performance in the three-dimensional target detection task, and the detection accuracy and recall rate have reached a high level.
Published Version
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