Off-duty detection method and device and storage medium
A detection method and technology to be detected, applied in neural learning methods, biological neural network models, office automation, etc., can solve the problems of low detection accuracy, high cost, and high computing resources.
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Embodiment 1
[0024] figure 1 A schematic flow chart of the method for leaving work detection according to this embodiment is shown.
[0025] refer to figure 1 As shown, the off-duty detection method of the present embodiment includes the following steps:
[0026] S102: Obtain a picture containing a human body image of an object to be detected;
[0027] S104: Using a feature extraction model based on machine learning training, determine the position information of the human body image in the picture; and
[0028] S106: According to the location information, determine whether the object to be detected leaves the post.
[0029] Specifically, a human body detection model based on a convolutional neural network is used to determine a human body image region including a human body target in an image to be detected. Then, the departure detection model based on the convolutional neural network is used to determine whether the human body target in the human body image area is in the off-duty st...
Embodiment 2
[0078] Figure 6 A schematic diagram of the departure detection device 600 described in this embodiment is shown. The off-duty detection device 600 of this embodiment corresponds to the method according to Embodiment 1.
[0079] refer to Figure 6 As shown, the device 600 includes: an acquisition module 610 for acquiring a picture of a human body image containing an object to be detected; a determination module 620 for determining the position information of the human body image in the picture by using a feature extraction model based on machine learning training and a judging module 630, configured to judge whether the object to be detected leaves the post according to the location information.
[0080] Optionally, the determining module 620 includes: a generating submodule, configured to generate a feature vector corresponding to the picture by using a neural network-based feature extraction model, wherein the first part of the feature vector elements is used to describe t...
Embodiment 3
[0086] Figure 7 A schematic diagram of the departure detection device 700 described in this embodiment is shown. The departure detection device 700 of this embodiment corresponds to the method according to Embodiment 1.
[0087] refer to Figure 7 As shown, the device 700 includes: a processor 710; and a memory 720, connected to the processor 710, for providing the processor 710 with instructions for processing the following processing steps: acquiring a picture containing a human body image of an object to be detected; utilizing a machine-based Learn and train the feature extraction model to determine the position information of the human body image in the picture; and according to the position information, determine whether the object to be detected has left the post.
[0088] Optionally, using a feature extraction model based on machine learning training to determine the position information of the human body image in the picture includes: using a neural network-based fe...
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