A rapid and accurate obstacle perception method for unmanned roller in construction based on millimeter-wave radar and camera fusion
The rapid and accurate perception of the construction environment by unmanned roller is essential for ensuring the safe and stable operation of the unmanned roller.However,in the current process of dam roller com-paction process,unmanned rollers rely solely on millimeter-wave radar to perceive the distance to obstacles.When the distance is less than a given threshold,the roller stops and waits.This approach fail to identify the category of obstacles,frequently resulting in the misidentification of obstacles,resulting the roller waiting for the subsequent rolling operations.To address the above issues,this study proposes a rapid and accurate obstacle perception method for unmanned roller in construction based on the fusion of millimeter-wave radar and camera.Firstly,the method replaces the convolutional kernel in the feature extraction network of the Faster R-CNN with dilated convo-lution kernels of different dilation rates to achieve rapid and accurate identification of dam surface obstacles.Subse-quently,the distance and velocity information of obstacles perceived by the millimeter-wave radar are fused with the category information perceived by the camera.This fusion achieves comprehensive and accurate perception of the dam surface environment.The Engineering cases indicate that the DC-Faster R-CNN object detection algorithm proposed in this study improves the mAP value by 2.59％compared to the traditional Faster R-CNN object detec-tion algorithm,and reduces the detection time per image by 0.015 s.Additionally,the perception strategy based on multi-modal fusion achieves precise obstacle avoidance during the dam compaction process,enhancing the safe-ty and efficiency of dam compaction construction.
millimeter-wave radarFaster R-CNNdilated convolutionperception fusionunmanned rollercompacting concrete dam
万方数据
维普
