41results about How to "Improve detection and recognition efficiency" patented technology

The invention provides a method and a device capable of continuously providing a sampling carrier, which are used for a hand-held wiping sampler. In the invention, the sampling carrier is manufactured into a long-strip shape, is folded into a layer shape according to certain length and sealed into a storage box, wherein one end of the storage box is provided with an outlet. A sampling carrier box is arranged on a special sampler, is pulled out for proper length to be fixed on a wiping surface when sampling, and a sample part is cut off for detection after the sampling. The continuous sampling carrier can keep straight and cannot be polluted in the processes of storage and application, is capable of sampling for multiple times by one-step mounting, simplifies the wiping sampling operation and improves the service efficiency of an ionic migration spectrometer.

The invention relates to a printing online real-time detection method of a smart card issuing system. The smart card issuing system is connected with an OCR detection system, and the printing online real-time detection is added based on the last process of printing in the card issuing process of the system. The printing online real-time detection method includes : First, after the card issuing system obtains the printing data of the smart card currently produced, it assembles it according to the pre-set data printing format and sends it to the OCR detection system; then the OCR detection system compares the actual printing information of the smart card according to the printing data and printing format Finally, return the result to the card issuing system; finally, the card issuing system will automatically reject the unqualified smart cards. In this way, the quality of smart card printing can be detected online in real time, and the problems in the printing process can be found automatically, timely and accurately. The detection and identification efficiency of smart card printing is high, and the quality of finished products is guaranteed; at the same time, the random inspection of operators is reduced. , reducing the workload of manual detection and reducing labor costs.

The invention relates to a method for realizing high-precision vehicle recognition based on a Loongson 2K1000 processor, belonging to the technical field of embedded image processing, including vehicle data collection and labeling processing, making deep learning data sets; training deep learning models; The learning model is optimized; the deep learning model is deployed; the video stream is selected for vehicle detection. The present invention realizes the AI ​​application deployment of vehicle detection and recognition on the Loongson 2K1000 processor by cutting and quantizing the model, calls the tmfile file on the 2K1000 processor; verifies for the first time that the 2K1000 processor supports yolo operators; adopts overall discarding for the feature module The method optimizes the performance of the model and improves the efficiency of vehicle detection and recognition; the invention has low cost, is suitable for technology update and replacement, is easy to deploy, and can be used for image recognition applications in other fields by making different data sets.

The invention relates to a fast and accurate recognition method for water meter measurement based on a slimSSD model, and belongs to the technical field of computer vision and pattern recognition. A fast and accurate recognition method for water meter measurement based on the slimSSD model described in the present invention includes data preparation, model improvement, model training, model optimization, model secondary fine-tuning, model testing and model output, in order to solve the water meter measurement area affected by water For the problem that noise interference such as fog cannot be accurately identified, a hybrid attention model is embedded in each detection branch to improve the SSD model, and combined with a saliency-driven channel clipping method to compress the network model, on the basis of ensuring accurate detection and identification of water meter metering areas, Remove redundant channel parameters, improve processing speed, and ensure portability of lightweight networks on embedded devices.

The present invention provides a method for detection and identification of direct spread signals, the steps of which include: 1. Calculate the power spectrum of the received signal, and perform logarithmic calculation on the power spectrum; then use the logarithmic calculation result as the detection 2. Filter the detection spectrum; 3. Find the maximum value Vmax in the filtered signal, and obtain the mean value Vmean of the filtered signal; 4. The ratio of the maximum value Vmax and the mean value Vmean is used as the detection feature value E, that is, E=Vmax / Vmean; 5. The detection feature value E is compared with the set threshold ethreshold to judge: if E>ethreshold, it is judged that the received signal is a direct spread signal . This method makes full use of the periodicity of the pseudocode component in the direct sequence spread spectrum signal in the frequency domain and time domain, and uses logarithmic operations to enhance the discrete component submerged in the continuous quantity, thereby reducing the impact of noise on the recognition performance. It is simple to implement, easy to implement in engineering, and has high signal identification efficiency.

A method and device for human target recognition provided by the present invention, the method comprising: obtaining a depth image; extracting image pixel features in the depth image; inputting the image pixel features into a human body deep learning model for identification and classification; Judging whether the classification of the image pixel features matches the existing human body part labels in the human body deep learning model; if the classification of the image pixel features matches the existing labels in the human body deep learning model, then the output and The label corresponding to the pixel feature. The invention adopts a deep learning model to identify image pixel features, and at the same time completes the detection and identification of human body objects, simplifies the detection and identification process, and improves the detection and identification efficiency.