Method for recognizing and locating for multi-target stone based on intelligent breaker operation scenario

With the continuous development of intelligent construction machinery, the traditional hydraulic breaker with complex structure and single function has been gradually replaced by the increasingly mature intelligent breaker. The recognition and localization of multi-target stones is an important guarantee for intelligent crushing to improve the accuracy of action output and complete the crushing task. A target stone recognition and localization method based on segmentation-mask convolutional neural networks (Mask R-CNN) instance segmentation and light detection and ranging (LiDAR) information fusion was proposed. The Mask R-CNN instance segmentation algorithm was used to quickly identify the region of interest (RoI) of the target stone in complex operation scenarios. On the premise of ensuring the accuracy of stone detection, the position information of the crushing point was obtained by fusing lidar and the Kalman filter algorithm to guide the crushing hammer to realize the positioning operation. The results of the field tests show that the average recognition accuracy of the stone target detection model for stones is 95.35%, the recall rate is 95.06%, and the accuracy of stone breaking point recognition is 94.20%, can meet the real-time requirements.