2results about How to "Various locations" patented technology

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.