Abstract: A method for collision warning implemented in an electronic device includes fusing obtained radar information and image information; recognizing at least one obstacle in a traveling direction of a vehicle according to the fused radar information and image information; determining motion parameters of the at least one obstacle and the vehicle according to the radar information and the image information; and calculating a collision time between the vehicle and the at least one obstacle according to the motion parameters, and issuing a collision warning.

Abstract: A method for determining a height of a plant, an electronic device, and a storage medium are disclosed. In the method, a target image is obtained by mapping an obtained color image with an obtained depth image. The electronic device processes the color image by using a pre-trained mobilenet-ssd network, obtains a detection box appearance of the plant, and extracts target contours of the plant to be detected from the detection box. The electronic device determines a depth value of each of pixel points in the target contour according to the target image. Target depth values are obtained by performing a de-noising on depth values of the pixel points, and a height of the plant to be detected is determined according to the target depth value. The method improves accuracy of height determination of a plant.

Abstract: A method for training a depth recognition model implemented in an electronic device includes determining test objects from test images, and obtaining a first image and a second image; calculating a test projection slope of each test object according to coordinates of each pixel point of each test object in the test images; generating a threshold range according to the plurality of test projection slopes; recognizing a type of terrain corresponding to a position of each initial object; adjusting an initial ground area in the first image, and obtaining a target ground area in the first image; generating a target height loss of a preset depth recognition network, an initial depth image corresponding to the first image, and the target ground area; and adjusting the preset depth recognition network according to the target height loss and a depth loss, and obtaining a depth recognition model for recognizing depth of images.

Abstract: A deep recognition model training method applied to an electronic device is provided. The method includes obtaining a ground plane area by segmenting a first image using a ground plane segmentation network. A projection image of the first image is generated based on the first image, an initial depth image corresponding to the first image, and a pose matrix. A target height loss of a depth recognition network is generated, and a depth loss of the depth recognition network is obtained according to a gradient loss between the initial depth image and the first image and a photometric loss between the projection image and the first image. A depth recognition model is obtained by adjusting the depth recognition network based on the depth loss and the target height loss.

Abstract: A method for reducing the error of depth estimation model comprises: obtaining a plurality of monocular images and a point cloud data of each of the plurality of monocular images, wherein each of the plurality of monocular images comprises an object frame image and a reference frame image; reconstructing the object frame image to obtain a reconstructed frame image according to the reference frame image and a first depth estimation model; determining a reconstructed error between the object frame image and the reconstructed frame image; and obtaining an inertia probability of each pixel of the object frame image according to speed information of the point cloud data and pixel information of the object frame image. This application provides more accurate depth estimation results for dynamic scenes. An electronic device and a non-transitory storage recording the method are also disclosed.

Abstract: An image depth prediction method acquires image frames of containing a dynamic object by a monocular camera, extracts a continuous of object frames and reference frames from the image frames, reconstructs the object frames to obtain reconstructed frames according to the reference frames and a preset depth estimation model, obtains a reconstruction error between the object frames and the reconstructed frames, processes the image frames to obtain point cloud data and instance segmentation data, fuses the point cloud data with the instance segmentation data to obtain mask data, obtains a loss function according to the reconstruction error and the mask data, and trains the depth estimation model based on the loss function until the loss function converges. The method can obtain more accurate depth estimation results for dynamic scenes. An electronic device and a non-transitory storage recording the method are also disclosed.

Abstract: An image depth recognition method held in a storage medium and running in a disclosed electronic device acquires images to be recognized and two original images. An original image is recognized through a depth recognition network to obtain an initial depth image, and a pose absolute value matrix is generated based on the two processed original images and a pose network, the pose network and the initial depth image generating an initial projection image. The processed two original images are recognized according to the pose absolute value matrix and the preset threshold matrix and adjusted based on errors between the initial depth image, the target image, and the target projection image. The depth recognition network obtains a depth recognition model, and the depth information of the image can be recognized. The method can improve the accuracy of the depth recognition of the image.

Abstract: The present application relates to image processing and provides a method for training an image depth recognition model, a method for recognizing image depth, and an electronic device. The method obtains static objects, dynamic objects, a dynamic position by performing an instance segmentation on the first image and the second image. A target dynamic object and a feature dynamic object are selected from the dynamic objects and the dynamic objects. A target image and a target projection image are generated according to the target dynamic object and the feature dynamic object. A depth recognition model is trained based on the target image, and the target projection image. The to-be-recognized image is recognized by the depth recognition model.

Abstract: A method for detecting a product for defects implemented in an electronic device includes detecting images of a product for defects by a first defect detection model in a preset period, and obtaining a detection result; when a ratio of the number of negative sample images is greater than a preset threshold, training an autoencoder model; obtaining historical positive sample images of the product, inputting the history positive sample images into the trained autoencoder model, and calculating a latent feature; inputting the latent feature of each history positive sample image into a decoding layer of the trained autoencoder model, and calculating newly added positive sample images; training the first defect detection model and obtain a second defect detection model; and inputting images of a product to be detected to the second defect detection model, and obtaining a detection result of the product.

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 defect of images applied in an electronic device inputs flawless sample training images into an autoencoder, and calculates first latent feature by a coding layer of the autoencoder, and calculates first reconstructed images by a decoding layer, and calculates a first reconstruction error by a first preset error function. The electronic device trains the discriminator according to the flawless sample training images and first reconstructed images, and calculates an adversarial learning error, and calculates a sample error, determines an error threshold based on the sample error, and obtains testing sample images, and calculates second latent feature of the testing sample images by the coding layer, and calculates the second reconstructed images of the testing sample images by the decoding layer, and calculate a difference between the testing sample images and the second reconstructed images, thus a detection result of the testing sample images is determined.

Abstract: A method for obtaining depth images implemented in an electronic device includes obtaining a first image and a second image; obtaining a predicted depth map of the first image, and calculating a first error value of the predicted depth map; determining a first transformation matrix between the first image and the second image; obtaining an instance segmentation image and obtaining a first mask image and a second mask image; obtaining a target transformation matrix; converting the predicted depth map into a first point cloud image, converting the first point cloud image into a second point cloud image, and converting the second point cloud image into a third image; calculating a second error value between the second image and the third image; obtaining a target deep learning network model; and inputting at least one image into the target deep learning network model, and obtaining at least one depth image.

Abstract: A method for managing driving applied in an electronic device which assesses distances to objects in a path of autonomous driving obtains RGB images of a scene in front of a vehicle, processes the RGB images based on a trained depth estimation model, and obtain depth images corresponding to the RGB images. The depth images are converted to 3D point cloud maps, 3D regions of interest from the 3D point cloud maps are determined according to a size of the vehicle, and the 3D regions of interest are converted into 2D regions of interest according to internal parameters of a camera. The 2D regions of interest are analyzed for obstacles. Driving continues when the 2D regions of interest have no obstacles, the vehicle is controlled to issue an alarm when obstacles are discovered.

Abstract: A method for assisting safer riving applied in a vehicle-mounted electronic device obtains RGB images of a scene in front of a vehicle when engine is running, processes the RGB images by a trained depth estimation model, obtains depth images and converts the depth images to three-dimensional (3D) point cloud maps. A curvature of the driving path of the vehicle is calculated, and 3D regions of interest of the vehicle are extracted from the 3D point cloud maps according to a size of the vehicle and the curvature or deviation from straight ahead. The 3D regions of interest are analyzed for presence of obstacles. When no obstacles are present, the vehicle is controlled to continue driving, when presence of at least one obstacle is determined, an alarm is issued.

Abstract: This application provides a method for optimizing a depth estimation model. The method includes obtaining a video of an object and capturing a first image and a second image from the video. An initial depth estimation model is obtained. An updated depth estimation model is obtained by performing an optimization process on the initial depth estimation model, and the optimization process is repeatedly performed on the updated depth estimation model. Once the updated depth estimation model meets predetermined requirements, the updated depth estimation model meeting predetermined requirements is determined as a target depth estimation model.

Abstract: A method for training a depth estimation model is provided. The method includes obtaining a first left image and a first right image. A disparity map is obtained by inputting the first left image into a depth estimation model. A second right image is obtained by adding the first left image to the disparity map. The first left image is converted into a third right image. A mask image is obtained by performing a binarization processing on a pixel value of each of pixel points of the third right image. Once a loss value of the depth estimation model is obtained by calculating a mean square error of pixel values of all corresponding pixel points of the first right image, the second right image, and the mask image, a depth estimation model is iteratively trained according to the loss value.

Abstract: A method for warning collision of vehicle is provided. The method obtains a pre-detected video. The pre-detected video includes a number of video frames, and the video frames are continuous. The method detects one or more vehicles in each of the video frames and determines a movement state of each of the vehicles via an optical flow method. The method detects one or more lane lines in each of the video frames to determine lane information, and determines whether to provide a collision warning according to the lane information and the movement state of each of the vehicles. A related vehicle and a non-transitory storage medium are provided.

Abstract: A vehicle-borne method for recognizing the illumination state of traffic lights even against a backlighting of strong sunlight or other light source obtains a first image of a set of traffic lights in a road traffic environment. A segmentation map is acquired by dividing a first region from the first image, and an illumination region in the segmentation map is extracted by marking RGB pixels in the region which are of a preset threshold in brightness according to a training model. A lit color of the set of traffic lights is recognized according to a position of the illumination region in the segmentation map. By utilizing the method, accuracy of recognition of illumination state of traffic lights is improved.

Abstract: An image defect detection method applied to an electronic device is provided. The method includes dividing a detecting image into a plurality of detecting areas, generating a detection accuracy value for each detecting area based on a defective image and a non-defective image, and obtaining a plurality of detection accuracy values. An autoencoder is selected for each detection accuracy value. A model corresponding to each detecting area is obtained by training the autoencoder based on the non-defective image. A plurality of reconstructed image blocks is obtained by inputting each of a plurality of detecting blocks into the corresponding model, and a reconstruction error value between each reconstructed image block and the corresponding detecting block is obtained. A detection result of a product contained in the image to be detected is obtained based on the reconstruction error value corresponding to each detecting block.

Abstract: A method acquires a first image and a second image of a target object being inputted into the depth estimation network for outputting a depth image. A pixel posture conversion relationship between the first image and the second image is obtained. The pixel posture conversion relationship includes a position relationship between each first pixel in the first image and a second pixel in the second image, which correspond to a same part of the target object. A restored image is generated based on the depth image, the pixel posture conversion relationship, and pre-obtained camera parameters. A loss of the depth estimation network is determined based on a difference between the first image, the depth image, the restored image, and the second image for adjusting the parameters of the depth estimation network. A training apparatus, and an electronic device applying the method are also disclosed.