6results about How to "Solve misidentification" patented technology

The invention discloses an intelligent detection method for attitude parameters of a photovoltaic power station assembly. The intelligent detection method comprises the following steps: (1) data acquisition and preprocessing; (2) detecting the corrected two-dimensional image by adopting the trained YOLO11 deep learning model; (3) establishing a transformation model from a two-dimensional image pixel coordinate to a three-dimensional model geographic coordinate, forming an accurate two-dimensional spatial index polygon by the transformed photovoltaic module array angular point coordinate points, and extracting a module array point cloud subset by using the polygon; and (4) for the problem that the extracted array point cloud subset contains a large amount of noise, an improved N-RG-RANSAC algorithm is adopted for processing. According to the method, the high-noise point cloud is effectively segmented and purified through the improved N-RG-RANSAC algorithm, so that high-precision and automatic extraction of the dip angle and the azimuth angle of the photovoltaic module is realized in a complex real scene environment, and the operation and maintenance efficiency of a photovoltaic power station is improved.

The warehouse alarm method and device, the electronic equipment and the computer readable storage medium provided by the application meet the same category recognition result of continuous multiple frames of pictures, and the difference between the time stamps corresponding to the adjacent two frames of pictures of the continuous multiple frames of pictures is less than the first threshold value, so that the recognition result of the continuous multiple frames of pictures is determined as a valid recognition result, the timeliness and accuracy of the recognition result are ensured, and then it is judged whether the staff wears a safety helmet based on the valid recognition result. Meanwhile, the state recognition of the warehouse operation equipment is improved, the misrecognition problem caused by the shaking of the recognition box is effectively avoided, and the false alarm rate is greatly reduced.

The present invention relates to a multi-robot linkage service control method driven by tourist behavior recognition, including: a first robot acquires a video image sequence of a target tourist, recognizes its visual behavior sequence and determines the service behavior state; when the target tourist meets a preset visual continuation trigger condition, visual continuation information is generated; the visual continuation information includes the visual appearance features, image position, target spatial position, moving direction, visual behavior sequence and service behavior state of the target tourist, and the target spatial position is obtained by coordinate mapping of the image position; the visual continuation information is sent to a second robot, and the second robot determines the corresponding target tourist in the video image it acquires based on this, and continues to recognize the visual behavior sequence and service behavior state of the target tourist; a multi-robot linkage service control instruction is generated according to the service behavior state after continuous recognition, and the robots participating in the linkage are controlled to execute service actions.

The present application relates to the technical field of digital recognition, and particularly provides a nixie tube content recognition method and device, electronic equipment and storage medium, wherein the method comprises: acquiring a continuous video image sequence of a nixie tube, acquiring position information of a nixie tube character display segment according to a preset frame number by using a template matching algorithm; recognizing bright and dark states of the nixie tube character display segment by using a support vector machine (SVM); acquiring a brightness sequence unit of the nixie tube character display segment under the preset frame number according to a preset nixie tube arrangement mode; classifying the brightness sequence unit by using a deep learning method and outputting a recognition result; and the device comprises: a nixie tube and a PC terminal connected with the nixie tube, the nixie tube and the PC terminal are configured in an asynchronous state, and the PC terminal comprises: an acquisition module, a position recognition unit, a brightness recognition unit and a training module. The purpose is to solve the misrecognition problem caused by uneven brightness and defects in the nixie tube content recognition process.

This invention relates to a method for detecting violations by personnel at conveyor belt stations based on an improved S2ANet and trajectory tracking. The method includes acquiring video and segmenting it into images frame by frame; inputting the images into the S2ANet-CBAM algorithm for conveyor belt target detection to obtain conveyor belt data; inputting the images into the YOLOv5 algorithm for worker target detection to obtain worker data; determining whether the conveyor belt is horizontal by comparing the conveyor belt data and worker data; using a Kalman filter to predict the position information of the worker and conveyor belt in the next frame; and using the Hungarian association algorithm to match and track the trajectories of the worker and conveyor belt. Finally, trajectory correction is performed, and the worker's trajectory and the conveyor belt trajectory are drawn, and the existence of violations is determined. This invention solves the problem of overlapping horizontal target boxes between the conveyor belt and the person in industrial applications due to the large size of the conveyor belt target, avoiding trajectory mismatch problems caused by target occlusion and errors caused by camera shake.

The invention discloses a welding track planning method based on physical modeling driving, which comprises the following steps of: S1, constructing a physical model for a workpiece welding seam based on a welding seam feature point track and a workpiece groove section shape, and generating a point cloud database of the welding seam by adopting a data augmentation method; s2, a laser vision sensor is adopted to collect point cloud on the surface of the welding workpiece; s3, constructing a point cloud segmentation model, performing training optimization on the point cloud segmentation model, and inferring the point cloud by using the optimized point cloud segmentation model to obtain a region of interest of the welding seam point cloud; s4, a welding seam point cloud is determined through the local orientation boundary box and the local concave-convex degree; s5, carrying out noise filtering and feature point extraction on the welding seam point cloud; and S6, track and posture planning is carried out on the extracted weld joint feature points, so that a track sequence executed by the welding robot is generated. According to the method, the problems of high cost and low efficiency of manual acquisition and marking of point cloud data are solved, and the robustness of weld feature extraction and the accuracy of trajectory planning are remarkably improved.