3results about How to "Improve target detection rate" patented technology

The invention relates to the technical field of crossing of vehicle electronic control and intelligent lighting, in particular to a basic lamp control function and ADAS bidirectional sensing collaborative self-adaptive lighting control system and a basic lamp control function and ADAS bidirectional sensing collaborative self-adaptive lighting control method. The system comprises an environment perception fusion module and a feature mapping and decision-making module, the environment perception fusion module maps a weight mask for environment brightness distribution data collected by an ALS, reduces the weight of an overexposure area, and injects the adjusted weight into a feature extraction layer in an ADAS system through the feature mapping and decision-making module. According to the method, the focusing of computing resources in the ADAS system in the effective dark area is realized, the sensing reliability is improved, the problem of ADAS sensing degradation in a strong light scene is solved, and the target detection rate under strong exposure is improved; the environment perception fusion module carries out target semantic segmentation and edge gradient enhancement on an original image collected by the ADAS system to obtain an adjusted probability value, and the feature mapping and decision-making module maps a light shape according to probability value distribution, so that the light shape control precision breakthrough is realized, and the LED illumination utilization rate and the light shape accuracy are improved.

The present application belongs to the technical field of radar signal processing, and particularly relates to an indoor fan interference distinguishing method based on millimeter wave radar signal autocorrelation. In order to overcome the problem of poor generalization performance of the current energy threshold method and convolutional neural network method in the field of indoor personnel detection, the present application provides an indoor head-shaking fan and human target distinguishing method based on autocorrelation function. The specific operation is to calculate the maximum correlation coefficient value of radar echo under different distance gates to determine the signal periodicity, and to distinguish human signals and fan signals by using the signal periodicity. The method can be applied to personnel detection methods in different indoor scenes, and can effectively improve the anti-interference ability of the radar system to the head-shaking fan.

This invention relates to a method for detecting the edge of weak moving infrared targets based on frame difference local contrast. The method identifies the main false alarm sources—high-brightness cloud edges, blind flash noise, and the inter-frame radiometric characteristics differences of the target. It enhances the target edge through three-frame temporal difference and an improved local contrast calculation method, removing false alarm sources such as flash noise, high-brightness background, and background edges. Then, based on the generated saliency map, threshold segmentation is used to obtain the target edge detection result, and finally, the original image is retrieved to obtain the final accurate target location. This invention addresses the difficulty in simultaneously meeting the requirements of detection rate and false alarm rate for weak infrared targets under complex dynamic backgrounds in practical engineering. Experimental results show that the proposed algorithm has high stability, effectively reducing the number of false alarms and improving target detectability while ensuring detection efficiency.