摘要
Harvesting robots had difficulty extracting filament phenotypes for small,numerous filaments,heavy cross-obscuration,and similar phenotypic characteristics with organs.Robots experience difficulty in localizing under near-colored backgrounds and fuzzy contour features.It cannot accurately harvest filaments for robots.Therefore,a method for detecting and locating filament picking points based on an improved DeepLabv3+algorithm is proposed in this study.A lightweight network structure,ShuffIetNetV2,was used to replace the backbone network Xception of the traditional DeepLabv3+.Convolutional branches for 3 different sampling rates were added to extract information on the safflower features under the receptive field.Convolutional block attention was incorporated into feature extraction at the coding and decoding layers to solve the interference problem of the near-color background in the feature-fusion process.Then,using the region of interest of the safflower branch obtained by the improved DeepLabv3+,an algorithm for filament picking-point localization was designed based on barycenter projection.The tests demonstrated that this method was capable of accurately localizing the filament.The mean pixel accuracy and mean intersection over union of the improved DeepLabv3+were 95.84%and 96.87%,respectively.The detection rate and weights file size required were superior to those of other algorithms.In the localization test,the depth-measurement distance between the depth camera and target safflower filament was 450 to 510 mm,which minimized the visual-localization error.The average localization and picking success rates were 92.50%and 90.83%,respectively.The results show that the proposed localization method offers a viable approach for accurate harvesting localization.
基金
supported in part by the National Natural Science Foundation of China[grant numbers 52265041 and 31901417]
the Open Subjects of Zhejiang Provincial Key Laboratory for Agricultural Intelligent Equipment and Robotics,China[grant number 202ZJZD2202]
the Graduate School-level Research and Innovation Program of Xinjiang Agricultural University,China[grant number XJAUGRI2023021].
