36249results about How to "Improve detection accuracy" patented technology

The invention relates to an external skeleton robot, in particular to a human-simulated external skeleton robot assisting the lower limbs. The human-simulated external skeleton robot assisting the lower limbs aims to solve the problems that an existing external skeleton robot is low in coupling degree of motion space and poor in wearing comfort, reliability and adaptation, and power needed by a motor is large. The human-simulated external skeleton robot assisting the lower limbs comprises an upper body back part, a left leg and a right leg. The left leg and the right leg respectively comprise a hip drive system, a knee drive system and a foot wearing system. A rear side connection board of the waist is in rotating connection with a load installation board. Each hip joint supporting board is provided with a first motor and a first reducer, wherein the first motor is provided with an encoder, and the output end of the first motor provided with the encoder is connected with the input end of the first reducer. Each hip joint connecting board can rotate in the vertical plane. Each thigh stretching board is in detachable connection with the corresponding hip joint connecting board. The output end of a main drive mechanism is connected with each crus connecting board. The lower surfaces of elastic boards are bonded with the upper surfaces of the rubber soles of the feet. The human-simulated external skeleton robot assisting the lower limbs can assist in walking.

An enterprise based workflow server based system to detect and classify images as potentially of pornographic nature. The system is based on two independent stages. The first stage is an automatic stage which eliminates most images that are positively non pornographic, including an image analysis engine with a plurality of sub engines, each dedicated to different attributes of an image. The second stage describes an additional embodiment of a manual review stage. An alternative embodiment describes a similar server solution to detect images suspected as copyrighted material. The images can be individually classified, or analyzed as a cluster or a collection of images such as an album or motion-picture segment, sampled into key still frames. The system optimizes time saving by elimination of images, and assures accuracy by generating a workflow reviewing solution which can involve a human decision-making stage.

A display device with a touch screen having display pixels and optical sensors each arranged in a matrix. In the display device, a backlight controller controls a backlight unit to be turned ON in a data display duration and controls the backlight unit to be turned OFF in a sensor detection duration other than the data display duration. Consequently, the influence of backlight on a touch part when the shade of the touch part is to be detected, can be reduced and an accuracy in the detection of an optical sensor can be increased

The invention provides a security detection method and system of an Android application program, aiming to solve the problems of an antivirus method of the existing Android platform that the scanning speed is slow and the false positive ratio is high. The method comprises the following steps of: scanning an Android installation package and extracting appointed characteristic information from the Android installation package; finding characteristic records which are matched with the appointed single characteristic information or a combination thereof from a pre-arranged security identification base, wherein the security identification base comprises the characteristic records and security grades corresponding to the characteristic records, and each characteristic record comprises single characteristic information or the combination of the characteristic information; and respectively displaying the security grade corresponding to the found characteristic records in a security detection result of the Android installation package. The security detection method and system of the Android application program disclosed by the invention have the advantages of fast scanning speed and high searching and killing accuracy.

The invention provides a method for detecting an X-ray mammary gland lesion image based on feature pyramid network under transfer learning. The method comprises the steps: 1, establishing a source domain data set and a target domain data set; 2, establishing a deformable convolution residual network layer by a deformable convolution and extended residual network module; 3, establishing a multi-scale feature extraction sub-network based on a feature pyramid structure through a feature map up-sampling and feature fusion method in combination with a deformable convolution residual network layer;4, establishing a deformable pooling sub-network sensitive to the focus position; 5, establishing a post-processing network layer to optimize a prediction result and a loss function; and 6, migratingthe training model to a small sample molybdenum target X-ray mammary gland focus detection task so as to improve the detection precision of the network model on the focus in the small sample image. According to the method, a transfer learning strategy is combined to realize focus image processing in a small sample medical image.

The invention provides an automatic vehicle detection method based on deep learning. The automatic vehicle detection method based on deep learning can utilize a convolution neural network to automatically extract the vehicle characteristics in the traffic environment so as to eventually realize automatic detection and identification of a vehicle target. The automatic vehicle detection method based on deep learning can be applied to different environments, different illumination intensities and different weather situations, and can guarantee higher accuracy for detection for a vehicle in a complicated traffic environment, and provides a new real-time reliable preprocessing step for identifying and tracking the vehicle target for the subsequent video monitoring.

The invention relates to the technical field of traffic image processing, and discloses a driving scene target detection method based on deep learning and multi-layer feature fusion. The method comprises the following steps of 1) collecting a video image to serve as a training data set, and performing preprocessing; 2) building a training network; 3) initializing the training network to obtain a pre-training model; 4) performing training on the training data set by using the pre-training model obtained in the step 3), thereby obtaining a training model; 5) collecting a front image by using a vehicle-mounted camera, and inputting the image into the training model obtained in the step 4), thereby obtaining a detection result. The multi-layer feature fusion method based on a feature pyramid is adopted to enhance semantic information of a low-layer feature graph, so that the feature extraction quality of the network is improved, and higher detection precision is obtained.

The invention provides a security detection method and system of an Android application program, solving the problems of low scanning speed and high false alarm rate of the traditional virus killing method of an Android platform. The security detection method comprises the steps of: scanning an Android installation package, extracting appointed characteristic information from the Android installation package; uploading the appointed characteristic information to a server, searching a characteristic record matched with appointed single characteristic information or combination from a security identification base preset in the server; and receiving a security detection result returned by the server aiming at the Android installation package, and displaying on a client end user interface, wherein the security detection result includes a security level corresponding to the characteristic record searched by the server. The client end detection is combined with the server detection, thus the security detection method is high in scanning speed and high in killing accuracy rate.