The construction of road tree detection network based on segment weighting

doi:10.13474/j.cnki.11-2246.2020.0048

Abstract

Abstract: In this paper, an end-to-end training network is proposed based on the framework of deep learning forobject detection, which can be used for automatic street treesdetection. Because of the occlusion problem between the roadway trees, the existing general object detection framework cannot be applied to this task directly. In this paper, a tree-shaped partial weighting module is proposed to reduce the error detection caused by severe occlusion. Then, the proposed neural network is trained and evaluated. The results show that the partitioned weighted tree detection network established in this paper can effectively detect the street trees in street scenes under occlusion conditions. The experimental results further show that the method gets high accuracy and good robustness under various conditions.

Key words: street tree, tree detection, convolutional neural network, deep learning, object detection

CLC Number:

P208

SHEN Yu, QIU Yuxuan, YU Zhenghao. The construction of road tree detection network based on segment weighting[J]. Bulletin of Surveying and Mapping, 2020, 0(2): 77-81,101.

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