171results about How to "Improve object detection accuracy" patented technology

The invention relates to a rapid target detection method based on a convolutional neural network, and relates to the computer vision technology. The rapid target detection method comprises the following steps: training convolutional neural network parameters by utilizing a training set; solving the problem of max-pooling losing feature by using an expander graph and generating a discriminative complete feature graph; regarding the full-connection weight of the convolutional neural network as a linear classifier, and estimating the generalization error of the linear classifier on the discriminative complete feature by using a probable approximately correct learning framework; estimating the required number of the linear classifiers according to the generalization error and the expected generalization error threshold value; and finally, completing the target detection on the discriminative complete feature graph by using the linear classifiers on the basis of a smooth window. The detection efficiency and the target detection precision are obviously improved.

A display includes: display pixel electrodes; common electrodes; a display layer; a display control circuit; touch detection electrodes; and a touch detection circuit detecting an external proximity object based on a detection signal obtained from the touch detection electrodes with use of a common drive voltage for display applied to the common electrode as a touch sensor drive signal. The touch detection circuit includes: a first filter allowing a fundamental detection signal, contained in the detection signal and having a frequency same as a fundamental frequency of the touch sensor drive signal, to pass therethrough, a plurality of second filters separately allowing two or more harmonic detection signals, contained in the detection signal and having frequencies same as respective harmonic frequencies of the touch sensor drive signal, to pass therethrough, and a detection section performing a detection operation based on the fundamental detection signal and the harmonic detection signals.

The invention discloses a regional average value kernel density estimation-based moving target detecting method in a dynamic scene. The method comprises the following steps of: firstly, initializing a background model; secondly, building a time and space background model for describing the dynamic complex scene by using a training sample in a background modelling process and considering the time sequence characteristics of pixel points in a video frame and the space characteristics in the adjacent regions of the pixel points; thirdly, continuously updating the background model by using the new video frame sample in a moving target detecting process; fourthly, adapting to the instantaneous background change by the regional kernel density estimating method and adapting to the continuous background change by using single Gauss background model, wherein the combination of the two models can fast and accurately adapt to the continuous change of the background and increases the executing efficiency of the method at the same time; and finally performing a foreground detecting method by providing an adjacent region information amount-based method so as to further remove noise points and inanition of a moving target in the background region in the detecting process and more completely extract the moving object in the foreground. The method can be widely applied to alarming the suspicious moving target in an intelligent monitoring system in an outdoor scene or a prohibited military zone and has wide market prospect and application value.

The invention discloses a target detection method, system, and device and a storage medium based on an area proposal. The method comprises the steps of inputting an image to be detected into a targetdetection network, receiving a final boundary frame outputted from the target detection network, and determining a target to be detected from the image to be detected according to the final boundary frame. The invention provides a novel target detection network, A target detection network includes a plurality of branches, Each branch contains local information and global information, and each branch continues to extract and learn feature information based on the processing results of the previous branch, so it can give attention to both local information and global information of the image, and can achieve high accuracy of target detection. The invention is widely applied to the technical field of image recognition.

The invention relates to a target detection method based on global and local information fusion, and belongs to the field of video image processing. The method comprises the following steps: firstly,sending a scene into a convolutional neural network to increase the memory ability of the network, so that the network better learns scene context information to obtain global scene features; secondly, establishing a relationship between objects in a self-adaptive manner by referring to an attention mechanism to obtain local object characteristics; and finally, fusing scene features and object features through information transmission to enhance feature expression. The method has the advantages that global scene features and local object features are considered at the same time, target features are better represented through information transmission, and a large number of contrast experiments show that the detection performance of the method is obviously superior to that of other target detection methods.

The invention relates to a three-dimensional target detection system based on laser point cloud; the system comprises a voxel size division module, a feature coding module, a feature extraction and fusion module, a target regression and detection module and a laser radar. The output end of the laser radar is connected with the input end of the target regression and detection module through the voxel size division module, the feature coding module and the feature extraction and fusion module in sequence, and during use, firstly, the voxel size division module performs voxel division on a three-dimensional target point cloud obtained from the laser radar by adopting different voxel scales; a plurality of voxelized point clouds are obtained, then feature coding is performed on the plurality of voxelized point clouds by a feature coding module; feature extraction and fusion are performed on the coded voxelized point clouds by a feature extraction and fusion module to obtain a final feature map; and finally, a three-dimensional target detection box is obtained by a target regression and detection module according to the final feature map. The design can guarantee that the structural features of the point cloud are not lost, and improves the detection precision of the three-dimensional target.

The invention discloses a picking point positioning method which comprises the following steps: obtaining a trained YOLOv3 target detection model, collecting and detecting a fruit growth area image, obtaining the number and positions of fruits in a detection view field, and fusing a feature extraction network of dense connection and residual idea with the target detection model; judging the numberof fruits in the visual field, and judging the images as a distant view and a close view according to the number; performing branch segmentation on the close-range image by using a semantic segmentation model to obtain a branch segmentation image; carrying out target detection on the distant view; judging whether the number of fruits in the branch segmentation image is 1 or not, if yes, using a single-fruit picking strategy, and if not, using a multi-fruit picking strategy; and acquiring final picking positioning point. According to the method, non-damage picking of mature bunches of fruits can be achieved, the shear point positions of the mature bunches of fruits are accurately found and positioned through an algorithm, fruit shear type picking is achieved, and integrity and picking efficiency of the fruits are guaranteed.