Abstract: The embodiment of the disclosure provides a method for training a pattern enhancing model and an electronic device. The method includes: reading a first pattern with a defect via a pattern reader, and determining whether the pattern reader reads successfully; if a reading is successful, performing a pattern reconstruction operation based on pattern information of the read first pattern to obtain a second pattern; training the pattern enhancing model based on the first pattern and the second pattern as a training data set.

Abstract: A sensing panel includes a substrate, a first sensor, a second sensor, a first switching element, and a second switching element. The first sensor includes a first metal electrode layer, a light-sensing layer, and a first transparent electrode layer. The first sensor is configured to receive a light and correspondingly generate a first sensing signal. The second sensor includes a second metal electrode layer, an insulating layer, and a second transparent electrode layer. The second sensor is configured to contact an object and generate a second sensing signal based on a capacitance value between the object and the second metal electrode layer. The first metal electrode layer is electrically connected to the second metal electrode layer and is electrically connected to the first switching element and the second switching element. The first transparent electrode layer is electrically connected with the second transparent electrode layer.

Abstract: A display panel including a circuit substrate, a plurality of light-emitting elements, a plurality of microlenses, and a plurality of dummy microlenses is provided. The circuit substrate is provided with a plurality of pixel areas. Each of the pixel areas is provided with the light-emitting elements. The plurality of microlenses are disposed on the circuit substrate and respectively overlapped with the light-emitting elements. The plurality of dummy microlenses are disposed between the microlenses and not overlapped with the light-emitting elements.

Abstract: The present disclosure provides a light emitting diode structure. The light emitting diode structure includes a substrate, a first light emitting structure on the substrate, a second light emitting structure on the substrate, and a third light emitting structure on the substrate. The first light emitting structure includes a first light emitting diode and a first focusing optic on the first light emitting diode. The second light emitting structure includes a second light emitting diode and a second focusing optic on the second light emitting diode. The third light emitting structure includes a third light emitting diode and a third focusing optic on the third light emitting diode. The first light emitting structure, the second light emitting structure and the third light emitting structure are arranged along a first direction and are dislocated in a second direction perpendicular to the first direction.

Abstract: An assistance method of safe driving applied in a vehicle-mounted electronic device obtains RGB images of scene in front of a vehicle, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images into three-dimensional (3D) point cloud maps, determines 3D regions of interest therein, and obtains position and size information of objects in the 3D regions of interest. When the position information satisfies a first preset condition and/or the size information satisfies a second preset condition, the presence of obstacles in the 3D regions of interest is determined and controls the vehicle to issue an alarm. When the position information does not satisfy the first preset condition and/or the size information does not satisfy the second preset condition, the 3D regions of interest are determined as obstacle-free, and permitting the vehicle to continue driving.

Abstract: A method for detecting road conditions applied in an electronic device obtains images of a scene in front of a vehicle, and inputs the images into a trained semantic segmentation model. The electronic device inputs the images into a backbone network for feature extraction and obtains a plurality of feature maps, inputs the feature maps into the head network, processes the feature maps by a first segmentation network of the head network, and outputs a first recognition result. The electronic device further processes the feature maps by a second segmentation network of the head network, and outputs a second recognition result, and determines whether the vehicle can continue to drive on safely according to the first recognition result and the second recognition result.

Abstract: A capacitive fingerprint sensor is provided. The capacitive fingerprint sensor includes a plurality of capacitive sensing pixels. Each of the capacitive sensing pixels includes a sensing node, a first transistor, a sensing capacitor and a second transistor. The first transistor has a first terminal receiving a first system voltage, a control terminal receiving a reset control signal, and a second terminal coupled to the sensing node. The sensing capacitor is coupled between the sensing node and a sensing control signal. The second transistor has a first terminal receiving a second system voltage, a control terminal receiving a sensing voltage of the sensing node, and a second terminal providing a sensing output signal.

Abstract: A model input size determination method, an electronic device and a storage medium are provided, the method includes acquiring a plurality of test images and a defect result; and encoding each test image to obtain an encoding vector. The encoding vector is decoded to obtain a reconstructed image, then a reconstruction error and a plurality of sub-vectors are calculated; the plurality of sub-vectors is inputted into a Gaussian mixture model, then a plurality of sub-probabilities, an estimated probability and a test error are determined; a detection result in the test image according to the test error and the corresponding error threshold are obtained; an accuracy according to the detection result and the defect result are determined, and an input size is selected from the plurality of preset sizes according to the accuracy. An accuracy of defect detection in manufacturing can be improved.

Abstract: A method applied in an electronic device for detecting and classifying apparent defects in images of products inputs an image to a trained autoencoder to obtain a reconstructed image, determines whether the image reveals defects based on a defect criterion for filtering out small noise reconstruction errors. If so revealed, the electronic device calculates a plurality of structural similarity values between the image and a plurality of template images with marked defect categories, determines a target defect category corresponding to the highest structural similarity value, and classifies the defect revealed in the image into the target defect category.

Abstract: A capacitive fingerprint sensor is provided. The capacitive fingerprint sensor includes a plurality of capacitive sensing pixels. Each of the capacitive sensing pixels includes a sensing node, a first transistor, a sensing capacitor and a second transistor. The first transistor has a first terminal receiving a first system voltage, a control terminal receiving a reset control signal, and a second terminal coupled to the sensing node. The sensing capacitor is coupled between the sensing node and a sensing control signal. The second transistor has a first terminal receiving a second system voltage, a control terminal receiving a sensing voltage of the sensing node, and a second terminal providing a sensing output signal.

Abstract: In a method for defecting surface defects, a trained weighting generated when defect-free training samples are used to train an autoencoder and pixel convolutional neural network is obtained. A test encoding feature is obtained by inputting the trained weighting into the autoencoder and pixel convolutional neural network and a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network encoding a test sample. The test encoding feature is input into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network to output a result of test. The test result is either no defect in the test sample or at least one defect in the test sample. Inaccurate determinations as to defects are thereby avoided. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: A method for identifying traffic sign is provided. The method obtains an original image of a traffic sign, and performs an image enhancing processing on the original image to form an enhanced image. The method performs a first conversion on the original image and the enhanced image to together form a first matrix of image data, and identifies a position of the traffic sign accordingly. The method further masks the original image and the enhanced image respectively to form a first identified region of the original image and a second identified region of the enhanced image accordingly. The method further performs a second conversion on the first identified region and the second identified region to together form a second matrix of the identified region of the image data, and identifies passage instruction information of the traffic sign accordingly. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: In a method for defecting surface defects, a weighting generated when defect-free training samples are used to train an autoencoder and pixel convolutional neural network is obtained. A test encoding feature is obtained by inputting the weighting into the autoencoder and pixel convolutional neural network and encoding a test sample with a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network. The test encoding feature is divided into many sub-test encoding features. The sub-test encoding features are input into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network one by one to output a result of test, the test result being either no defect in the test sample or at least one defect in the test sample. Inaccurate defect determinations are avoided, and accurate determinations even of fine defects improved. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: A method for detecting defects in multi-scale images and a computing device applying the method acquires a to-be-detected image and converts the to-be-detected image into a plurality of target images of preset sizes. Feature extraction is performed on each target image by using a pre-trained encoder to obtain a latent vector, the latent vector of each target image is inputted into a decoder corresponding to the encoder to obtain a reconstructed image and then into a pre-trained Gaussian mixture model to obtain an estimated probability. Reconstruction error is calculated according to each target image and the corresponding reconstructed image. A total error is calculated according to the reconstruction error of each target image and the corresponding estimated probability, and a detection result is determined according to the total error of each target image and a corresponding preset threshold, thereby improving an accuracy of defect detection.

Abstract: A method of detecting defects revealed in images of products obtains sample image training data. An underlying feature dimension of an autoencoder is selected and a score is obtained. By comparing the score with a standard score, an optimal underlying feature dimension is confirmed. A test image is inputted into the autoencoder with the optimal underlying feature dimension to obtain a reconstruction image. A reconstruction error between the test image and the reconstruction image is computed. By comparing the reconstruction error with the predefined threshold a result of analysis of the test image is outputted. An image defect detection apparatus and a computer readable storage medium applying the method are also provided.

Abstract: A movement detection method, applied to a navigation input device with a navigation pattern comprising a center pattern and a radial pattern. The movement detection method comprises: (a)capturing a sensing image comprising a center pattern image and at least portion of a radial pattern image by an image sensor, wherein the center pattern image corresponds to the center pattern and the radial pattern image corresponding to the radial pattern; (b)computing a translation of the navigation input device according to shift of the center pattern image; and (c)computing a rotation angle of the navigation input device according to a first pattern relation between the center pattern image and a first portion of the radial pattern image. The translation and the rotation angle can be precisely and sensitively detected even if the joystick device is miniaturized, since the translation and the rotation angle are computed according to the navigation pattern.

Abstract: The present disclosure provides a picking device which is suitable for absorbing at least one material. The picking device includes a vacuum source and a picking mechanism. The vacuum source is configured to pump air so as to provide negative pressure. The picking mechanism is connected with the vacuum source and includes a main body and a plurality of picking assemblies which are movably arranged on the main body. The main body is provided with a plurality of air channels for connecting the vacuum source and the picking assemblies respectively. Each picking assembly includes a picking piece, a moving piece and a blocking piece; the picking pieces and the moving pieces penetrate through the main body at intervals and pass through the corresponding air channels; the moving pieces are provided with air vents for moving along with the blocking pieces so as to communicate with the air channels.

Abstract: An image defect detection method is used in an electronic device. The electronic device determines a training image feature set, and trains a Gaussian mixture model by using the feature set to obtain an image defect detection model and a reference error value. An image for analysis is input into the autoencoder to obtain a second implicit vector and a second reconstructed image, and to calculate a second reconstruction error. The electronic device obtains a test image feature of the image for analysis according to the second reconstruction error and the second implicit vector, and inputs the test image feature into the image defect detection model to obtain a prediction score. The image for analysis is determined to reveal a defect when the prediction score is less than or equal to the reference error value.

Abstract: A lane line recognition method applied to an electronic device is provided. In the method, the electronic device obtains a target image comprising lane lines, and converts the target image into an aerial view of the lane lines. Once a left lane line curve and a right lane line curve are obtained by performing a curve fitting on the lane lines according to pixel points of the aerial view, the electronic device determines whether a recognition of the lane lines is accurate according to the left lane line curve and the right lane line curve.

Abstract: A defect detection method based on an image of products and an electronic device can accurately determine the error threshold by determining the reconstruction error generated during image reconstruction and by determining the estimated probability generated by the Gaussian mixture model. The test error can then be compared with the error, since the test error and the error threshold are compared numerically, the existence of subtle defects are revealed in the product image, thereby improving the accuracy of defect detection.