5results about How to "Various locations" patented technology

The present application discloses a license plate recognition method, an electronic device and a storage medium. The license plate recognition method includes: obtaining at least one initial image feature, each initial image feature is obtained by extracting the features of the image to be detected collected from the license plate; based on each initial image feature, determining the character offset information about the license plate in each initial image feature and the position information of the area of ​​interest, the area of ​​interest is the area where the license plate is located in the image to be detected, and each character offset information is used to represent the position relationship between at least part of the characters in the image to be detected and the preset position in the license plate; based on each character offset information and the position information of each area of ​​interest, determining the license plate recognition result of the image to be detected, the license plate recognition result includes at least one of the following: the type of the license plate, the area where the license plate is located in the image to be detected, and the character information in the license plate. The above scheme can improve the accuracy of license plate recognition.

The invention belongs to the technical field of detection devices, and discloses a magnetic shoe interior detection device and method, and the device comprises a detection frame, a transmission structure is mounted in the detection frame, a positioning assembly is arranged above the transmission structure, a mounting frame is fixedly mounted at the top end of the detection frame, and a detection structure is mounted at the bottom end of the mounting frame. The detection structure is arranged above the detection frame, a first rotating shaft is mounted in the transmission structure, a first gear is fixedly mounted on the outer wall of the first rotating shaft, a second gear is in meshed connection with one side of the first gear, a second rotating shaft is fixedly mounted in the second gear, and a motor is arranged at one end of the second rotating shaft; when the magnetic shoe is detected, the magnetic shoe is firstly placed at the top end of the transmission structure, then the transmission structure works to transmit the magnetic shoe to one side of the detection structure, the magnetic shoe is stopped after moving to the position below the detection structure, and finally the static magnetic shoe is detected through the detection structure, so that the accuracy of a magnetic shoe detection result is improved.

The application provides a construction worker unsafe behavior intelligent identification method and early warning system, the method comprises the following steps: obtaining a to-be-detected image of a construction site; inputting the to-be-detected image into a construction worker unsafe behavior identification model to obtain an unsafe behavior identification result of a construction worker; wherein the unsafe behavior at least includes an unworn state, a wrong wearing state or a blocked state of personal protective equipment; and the construction worker unsafe behavior identification model is a neural network model for target detection. The detection accuracy is significantly improved, and the fine-grained identification capability is enhanced. Since a learnable position coding component is introduced into the backbone network, the model can explicitly model the spatial position relationship of the feature map, enhance the spatial position perception capability of the personnel (especially small targets and blocked targets) in the construction scene under complex background interference, solve the problem that the traditional convolutional network is not sensitive to absolute position information, and improve the feature extraction accuracy in a complex background.

The application relates to a target detection method, device and computer equipment under a complex traffic scene. The method comprises the following steps: determining a detection result of a to-be-detected image based on a traffic target detection model; a mixed encoder layer of the traffic target detection model comprises an FDPN-P2 network; the detection result of the to-be-detected image is determined based on the traffic target detection model, which comprises the following steps: inputting original features obtained by processing the to-be-detected image into a plurality of residual blocks of a backbone network to obtain scale features,, and ; based on a feature fusion unit and a RepC3 unit of the FDPN-P2 network, the scale features and and the scale features after self-attention processing are subjected to fusion processing to obtain multi-scale features, and, and the multi-scale features after preprocessing and the scale features are fused to obtain multi-scale features; a plurality of Tokens are obtained based on the multi-scale features,, and, and a detection result of the to-be-detected image is obtained based on the plurality of Tokens. The method can improve the reliability of target detection under a complex traffic scene.

The invention discloses a remote sensing image building detection method, which belongs to the technical field of deep learning, and designs an improved YOLOv7 network architecture on the basis of a YOLOv7 algorithm in combination with a coordinate attention sensing module, a multi-scale residual module and a feature-refined dense connection feature pyramid module. Through the innovative design, the interference of a complex background is further reduced, and the problems caused by the size change of a building and unobvious characteristics of a small building are effectively relieved, so that the robustness and precision of target detection are improved.