42results about How to "Improve detection robustness" patented technology

The invention discloses a multi-task deep convolutional neural network-based vehicle color identification system. The system comprises a high-definition camera mounted above lanes on a road, a trafficcloud server and a vehicle color visual detection subsystem; the vehicle color visual detection subsystem comprises a vehicle locating detection module, a license plate locating detection module, a license plate background color identification module, a color difference calculation module, a vehicle color correction module and a vehicle color identification module; the vehicle locating detectionmodule, the license plate locating detection module and the vehicle color identification module share a same Faster R-CNN deep convolutional neural network; by adopting the deep convolutional neural network, vehicles on the road are quickly segmented; license plates on the road are quickly segmented by further adopting the deep convolutional neural network through using vehicle images; and space position information of the vehicles and the license plates in a road image is given. The multi-task deep convolutional neural network-based vehicle color identification system provided by the invention is relatively high in detection precision and relatively high in robustness.

The invention relates to a cloth defect detection method based on Fourier transform and image morphology. The cloth defect detection method comprises the following steps: (1) collecting an original image of cloth by using a linear array camera; (2) preprocessing the original image; (3) extracting image texture features by using LAWS texture filtering; (4) adopting a GMM classifier model to carry out defect pre-judgment; (5) constructing a band elimination filter by using a Gaussian filter; (6) generating a defect image by using Fourier transform and inverse transform; (7) extracting flaw positions and areas by adopting image morphology; And (8) outputting a cloth detection result. According to the method, more than ten types of cloth flaws including broken wefts, broken warps, broken holes, floating wefts and the like can be detected in real time, the detection speed is high, the accuracy rate is high, and the detection robustness is enhanced through the image processing method integrating the airspace and the frequency domain.

The invention provides a barrier detection device based on pattern projection. The device comprises a pattern generator, an image sensor and a barrier detection component, wherein the pattern generator is used for projecting a pattern to a pavement; the image sensor acquires pavement image information with the pattern; the barrier detection component judges whether a barrier exists on the pavement by analyzing a change situation of the pattern in the acquired image. According to the device, a pattern projection mode is adopted during barrier detection, so that the complexity of barrier analysis and detection is greatly simplified, the cost is reduced, the robustness of barrier detection is improved, and the detection speed and the precision are improved.

The invention relates to a mine belt anomaly detection method and device based on video processing. A high-definition camera is installed at a coal drop port between the belt conveyors. The camera shoots a picture directly opposite the coal drop port, and the video information of the coal drop port is collected in real time, and the processing process is visualized. The method comprises the following steps of: preprocessing a frame image extracted from a monitoring video of a coal nozzle, wherein the preprocessing comprises image grayscale transformation and filtering; Calculating the ratio ofthe number of pixels nc of coal particles in the preprocessed image to the total number of pixels n of the delimited detection area to obtain the relative area lambda s; When the relative area lambdas is greater than the first threshold value Ts1, determining that there is a large coal block or a coal piling condition in the image; the first threshold value Ts1 has a range of 0<Ts1<1. The invention can intelligently detect the possible large coal lump or coal piling at the coal nozzle, and the method can enhance the detection robustness, reduce the calculation amount, reduce the calculationerror, and speed up the operation.

The invention discloses a contact point real-time detection algorithm based on a monocular infrared image. The method comprises the steps of using a bow template intercepted from a first original image of an infrared video frame sequence for carrying out template matching on a monocular infrared original image obtained in real time, and determining the position of a pantograph; according to the position of the pantograph, respectively extracting an image of a contact line above the pantograph and an image of an area of interest of the pantograph from the monocular infrared original image; respectively performing image enhancement operation on the contact line image above the bow and the image of interest of the pantograph to obtain a contact line enhanced image and a pantograph enhanced image; performing pixel normalization operation and parameter optimization on the contact line enhanced image and the pantograph enhanced image respectively, and extracting a contact line point set anda pantograph carbon slide plate contour point set; and respectively carrying out linear parameter estimation on the contact line point set and the pantograph carbon slide plate contour point set, andcalculating an intersection point of a contact line fitting straight line and a pantograph slide plate fitting straight line to realize the contact point positioning.

The invention relates to an optical remote sensing image marine ship target extraction and identification method. The method comprises the following steps: inputting a visible light remote sensing image; performing global detection on the sea surface in the remote sensing image by using a visual saliency method based on covariance joint features to generate a single-scale saliency map; performing downlink sampling on the generated single-scale saliency map, establishing a multi-scale saliency map, and obtaining a final saliency map; calculating a detection threshold value of a sea surface ship target, binarizing the saliency map, and realizing coarse segmentation of the saliency map; establishing a training set and a test set; the feature vectors can be used for training and testing; training a model according to the established framework and positive and negative samples in the training set, and classifying the feature vectors; and according to the established framework and positive and negative samples in the test set, testing the candidate area, and completing the extraction and identification of the optical remote sensing image marine ship target. The sea surface target area can be efficiently searched, the false alarm rate is greatly reduced, and the detection accuracy is improved.

The invention discloses a warehouse cargo multi-QR code integrated visual positioning method, and relates to the field of image detection, in particular to a multi-QR code integrated visual positioning method. The technical problem that an existing detection method cannot accurately position the QR code because of complex environment where the QR code is located and multi-angle shooting is solved.The method comprises the following steps: filtering an image; converting the QR code into a grey-scale map, binarizing the grey-scale map, roughly locating the position of a position detection graphaccording to closed contour information in the binary image, finely locating the position detection graph according to the characteristic that the areas of three position detection graphs in the sameQR code presented in the image are similar, and finally locating the QR code. According to the method, a plurality of two-dimensional codes in a scene can be accurately positioned from a plurality ofshooting angles in a complex environment, the robustness is relatively high, and the method has very good performance in actual use.

The invention provides a personnel leaving detection system. The personnel leaving detection system comprises an ultra wide band main node and at least one ultra wide band auxiliary node which are arranged in a vehicle, wherein each ultra wide band auxiliary node is used for collecting channel impulse response (CIR) information generated by communication with the ultra wide band main node, and sending the CIR information to the ultra wide band main node; the ultra wide band main node is used for detecting whether a vehicle is flamed out and locked or not: if yes, initiating periodic ultra wide band broadcasting to establish communication with all the ultra wide band auxiliary nodes, and after CIR information sent by all the ultra wide band auxiliary nodes in the current broadcasting period is received, operating a personnel leaving detection algorithm to judge whether the personnel are left or not according to all the received CIR information. Due to the fact that the ultra-wideband signals are not easily affected by human body and environment shielding, and the ultra-wideband universal chip has a CIR information analysis function and does not need chip firmware refreshing, hardware cost is low, detection robustness is high, in addition, biological characteristic data collection does not need to be carried out, and user privacy is also protected.

The embodiment of the invention discloses a mycobacterium tuberculosis detection model training method and device, equipment and a storage medium. The method comprises the following steps: acquiring a first preset number of first images and annotation information of each first image, namely the position of mycobacterium tuberculosis in the first image; then, cutting out a second preset number of second images with different sizes from each first image, namely taking part of the first images as the second images, and zooming all the second images containing mycobacterium tuberculosis into third images with the same size to simulate the shooting effects of different shooting devices; then, constructing a sample data set for training the network by using all the third images; and finally, training a preset target detection network according to the sample data set to obtain a mycobacterium tuberculosis detection model. Therefore, according to the embodiment of the invention, the sample input into the preset target detection network can simulate different shooting equipment to shoot the image containing the mycobacterium tuberculosis, so that the detection robustness of the preset target detection network is improved.