Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor technologies.To achieve high performance and low cost,perce...Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor technologies.To achieve high performance and low cost,perception tasks are of great importance.In this study,a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural machinery.The approach of this study utilized point clouds,with low-resolution Lidar point clouds as inputs,generating high-resolution point clouds and Bird's Eye View(BEV)images that were encoded with several basic statistics.Using a BEV representation,road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++neural network.Three enhancements were proposed for U-Net++:1)replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block(ACBlock);2)adding a multi-branch Asymmetric Dilated Convolutional Block(MADC)in the highest semantic information layer;3)adding an Attention Gate(AG)model to the long-skip-connection in the decoding stage.The results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds,which was 7.35 percentage points higher than U-Net++.Furthermore,the average processing time of the model was about 70 ms,meeting the time requirements of unmanned driving in agricultural machinery.The proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.展开更多
The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs.To achieve high performance,perception tasks(such as obstacle detection,...The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs.To achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great importance.Compared with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature.This study proposed an extraction method for the straight field roads between farmlands.The proposed method was based on the point cloud data acquired by LiDAR(Velodyne VLP-16)mounted on a John Deere 12046B-1204 tractor.The proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment modification.Firstly,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road points.Secondly,the boundary lines constraint was constructed to extract the distant road curve segments.Thirdly,the local distance ratio was used to modify the extracted road curve segments.The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and effective.The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.展开更多
基金financially supported by the National Key R&D Program of China and Shandong Province,China(Grant No.2021YFB3901300).
文摘Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor technologies.To achieve high performance and low cost,perception tasks are of great importance.In this study,a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural machinery.The approach of this study utilized point clouds,with low-resolution Lidar point clouds as inputs,generating high-resolution point clouds and Bird's Eye View(BEV)images that were encoded with several basic statistics.Using a BEV representation,road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++neural network.Three enhancements were proposed for U-Net++:1)replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block(ACBlock);2)adding a multi-branch Asymmetric Dilated Convolutional Block(MADC)in the highest semantic information layer;3)adding an Attention Gate(AG)model to the long-skip-connection in the decoding stage.The results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds,which was 7.35 percentage points higher than U-Net++.Furthermore,the average processing time of the model was about 70 ms,meeting the time requirements of unmanned driving in agricultural machinery.The proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.
基金financially supported by the National Key Research&Development Project(Grant No.2021YFB3901302)the Beijing Municipal Science and Technology Project(Grant No.Z201100008020008).
文摘The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs.To achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great importance.Compared with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature.This study proposed an extraction method for the straight field roads between farmlands.The proposed method was based on the point cloud data acquired by LiDAR(Velodyne VLP-16)mounted on a John Deere 12046B-1204 tractor.The proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment modification.Firstly,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road points.Secondly,the boundary lines constraint was constructed to extract the distant road curve segments.Thirdly,the local distance ratio was used to modify the extracted road curve segments.The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and effective.The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.
