A circuit device 100 includes an error detection circuit 110 and a processing circuit 120. The error detection circuit 110 obtains a glare index value, which is an index value indicating glare of a head-up display, based on image data IMD for head-up display. The error detection circuit 110 determines whether or not a glare index value has exceeded a first threshold value, and when the glare index value exceeds the first threshold value, detects occurrence of a first glare error. When occurrence of a first glare error is detected, the processing circuit 120 performs processing corresponding to the first glare error.

The present disclosure describes human body attribute recognition methods and apparatus, electronic devices, and a storage medium. The method includes acquiring a sample image containing a plurality of to-be-detected areas being labeled with true values of human body attributes; generating, through a recognition model, a heat map of the sample image and heat maps of the to-be-detected areas to obtain a global heat map and local heat maps; fusing the global and the local heat maps to obtain a fused image, and performing human body attribute recognition on the fused image to obtain predicted values; determining a focus area of each type of human body attribute according to the global and the local heat maps; correcting the recognition model by using the focus area, the true values, and the predicted values; and performing, based on the corrected recognition model, human body attribute recognition on a to-be-recognized image.

The present disclosure describes human body attribute recognition methods and apparatus, electronic devices, and a storage medium. The method includes acquiring a sample image containing a plurality of to-be-detected areas being labeled with true values of human body attributes; generating, through a recognition model, a heat map of the sample image and heat maps of the to-be-detected areas to obtain a global heat map and local heat maps; fusing the global and the local heat maps to obtain a fused image, and performing human body attribute recognition on the fused image to obtain predicted values; determining a focus area of each type of human body attribute according to the global and the local heat maps; correcting the recognition model by using the focus area, the true values, and the predicted values; and performing, based on the corrected recognition model, human body attribute recognition on a to-be-recognized image.

There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, and a signal processing unit, which simultaneously outputs, as sensing data, pieces of detection data respectively output from the plurality of sensors on the basis of the sensing period of any one of the plurality of sensors, determines whether each region of an outer cover corresponding to a location of each of the plurality of sensors is contaminated on the basis of the pieces of detection data, and outputs a determination result as contamination data.

An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, and a signal processing unit, which simultaneously outputs, as sensing data, pieces of detection data respectively output from the plurality of sensors on the basis of the sensing period of any one of the plurality of sensors, determines whether each region of an outer cover corresponding to a location of each of the plurality of sensors is contaminated on the basis of the pieces of detection data, and outputs a determination result as contamination data.

The technology of this application relates to an artificial intelligence terminal-based image detection method and apparatus, and an electronic device. The method includes obtaining a to-be-detected image, determining a light source region of the to-be-detected image and a foreground region of the to-be-detected image, and determining a blurring degree of the to-be-detected image based on the light source region and the foreground region. Impact of a light source on clarity of the to-be-detected image is determined by performing light source region detection and foreground region detection on the to-be-detected image, to effectively detect whether an image shot in a backlight condition is blurred.

The technology of this application relates to an artificial intelligence terminal-based image detection method and apparatus, and an electronic device. The method includes obtaining a to-be-detected image, determining a light source region of the to-be-detected image and a foreground region of the to-be-detected image, and determining a blurring degree of the to-be-detected image based on the light source region and the foreground region. Impact of a light source on clarity of the to-be-detected image is determined by performing light source region detection and foreground region detection on the to-be-detected image, to effectively detect whether an image shot in a backlight condition is blurred.

A three-dimensional model generation method executed by an information processing device includes: obtaining images generated by shooting a subject from respective viewpoints; searching for a similar point that is similar to a first point in a first image among the images, from second points in a search area in a second image different from the first image, the search area being provided based on the first point; calculating an accuracy of a search result of the searching, using degrees of similarity between the first point and the respective second points; and generating a three-dimensional model using the search result and the accuracy.