3results about How to "Reduce image size" patented technology

The present invention relates to the field of partition wall quality detection, and proposes a method and system for realizing the apparent quality detection of lightweight partition walls based on deep learning. The method comprises: obtaining an apparent image of an installed partition wall, marking an image label, constructing an apparent image set, performing image smoothing processing on the apparent image set to obtain a smoothed image set, identifying grayscale and querying the pixel quantity, calculating an apparent histogram, calculating the cumulative distribution value of each grayscale in the apparent histogram, and based on these cumulative distribution values, performing regional segmentation on the partition wall apparent image using a pre-constructed partition wall apparent quality inspection model to obtain an apparent segmented region, and calculating a texture feature value, performing clustering processing on the texture feature to obtain a partition wall apparent quality category, performing quality marking on the apparent segmented region according to the apparent quality category, and generating an apparent quality inspection report of the installed lightweight partition wall according to the regional quality marking. The present invention can improve the apparent quality detection efficiency of lightweight partition walls.

The present application relates to the field of signal detection, and particularly to a non-cooperative unmanned aerial vehicle burst frequency point detection method and device based on a Seg-Yolo model. The Seg-Yolo model proposed by the present application does not require prior sequences of non-cooperative unmanned aerial vehicle signals at all, and does not require prediction of specific sequences, and can directly realize continuous, discontinuous, overlapping and non-overlapping signal frequency point detection in a time-frequency graph. Through preprocessing, image noise reduction and the Seg-Yolo model, the present application reduces the image volume that needs to be processed and simplifies the size of the deep learning network, can accurately lock the center frequency of each frequency point on the basis of reducing the training time and improving the training efficiency, and improves the accuracy and reliability of signal extraction based on complex frequency hopping signals.

This invention discloses a method for large-scale online detection and counting of orchard fruits using drones. It utilizes drones to collect video footage of orchards; establishes a drone live-streaming platform for online, real-time video transmission; builds a fruit detection model to perform real-time detection of the drone video footage, obtaining the center point and detection frame position information of each fruit; employs the DeepSORT algorithm to assign a specific ID to each fruit in the drone video footage and tracks them across frames; and constructs a fruit counter to achieve global calculation of the orchard fruit count. This invention overcomes the limitation that drones can only perform offline operations in orchard scenarios, and simultaneously solves the problems of missing small-sized fruits and severely occluded fruits within the drone's field of view, providing farmers with effective information for subsequent decision-making in a short period of time.