6results about How to "Avoid erasure" patented technology

Embodiments of the present application provide an IAP upgrading method, device, electronic equipment and storage medium. The IAP upgrading method comprises: during running a starting program, when a upgrading instruction is read, acquiring an application program; reading program storage occupation information of the starting program; determining a first physical address range corresponding to the starting program based on the program storage occupation information, the first physical address range comprising a first physical end address; and offsetting to the first physical end address to burn the application program. In the embodiments of the present application, the first physical address range corresponding to the starting program is determined based on the program storage occupation information of the starting program, the starting program and the application program are partitioned in the physical address, the application program is burned by offsetting to the end address, the physical address of the starting program is locked, the starting program is prevented from being erased by the application program, the invalidation risk of the starting program is reduced, the implementation of the upgrading process is ensured, and the IAP upgrading success rate is improved.

The present application relates to the technical field of image processing, in particular to a fisheye lens image pixel-level edge enhancement and denoising method, comprising: a data acquisition step: acquiring original fisheye image data and preset fisheye lens optical distortion parameters; a weight generation step: determining the distortion stretching rate of a pixel point according to the optical distortion parameters; and generating a spatial density weight map; a threshold determination and denoising step: determining a target denoising threshold according to the spatial density weight map; denoising the original fisheye image data to generate an intermediate denoising image; a convolution kernel generation step: generating an adaptive curved surface convolution kernel according to the optical distortion parameters; an edge enhancement step: using the adaptive curved surface convolution kernel to perform edge enhancement processing on the intermediate denoising image; and generating an enhanced image; the present application effectively avoids the risk of irreversible erasure of key semantic features in the edge field, and ensures high-reliability feature preservation in the extreme field.

This invention discloses a method for detecting minute defects in cigarette boxes based on a CDM-YOLOv8 network, comprising: Step 1, acquiring an image of the cigarette box surface and constructing a defect detection dataset; Step 2, performing feature extraction enhancement on the input image, extracting defect-related features through multi-branch convolution, deformable convolution, and spatial attention mechanisms; Step 3, dynamically fusing features at different scales, and performing frequency domain banding processing on the fused features during the downsampling stage, employing a differentiated downsampling strategy to preserve defect edge and texture information; Step 4, outputting the category and location information of the defects based on the detection head. This invention solves the problems of easy loss of minute defects in cigarette boxes during downsampling and easy missed detection in complex backgrounds. By enhancing feature representation capabilities and preserving high-frequency defect information while ensuring computational efficiency, it improves the detection accuracy of minute defects in cigarette boxes and reduces the missed detection rate.

This invention discloses a video action recognition method using a time-progressive feature fusion token dictionary modeling approach, comprising: acquiring an original video frame sequence; generating multiple multi-scale video segments containing different time scales based on a time-progressive strategy; processing the multi-scale video segments separately using a feature extraction model to extract local spatiotemporal features corresponding to each time scale; progressively interacting the local spatiotemporal features of each scale with a pre-initialized auxiliary token dictionary in ascending order of time scale, dynamically updating the auxiliary token dictionary in each interaction to generate enhanced spatiotemporal features corresponding to each scale; dynamically generating fusion weights for the enhanced spatiotemporal features of multiple scales and performing feature fusion to obtain global multi-scale fused features; and outputting action classification results using a classification model based on these features. This invention solves the technical problems of insufficient long-term dependency modeling, semantic drift, and interference from complex backgrounds in existing technologies.

The invention relates to an electric power machine patrol image enhancement method and device based on semantic consistency and reinforcement learning. The method comprises the following steps: acquiring an original electric power inspection image, inputting the original electric power inspection image into a semantic segmentation network, and extracting noise components in the original electric power inspection image; component carrying information of the noise component is collected, and the information missing degree of the denoised original electric power inspection image is determined; collecting shielding information of noise components, and determining an artifact introduction degree of the de-noised original electric power inspection image; determining an associated image of the original electric power inspection image, and calculating a linkage observation degree between the de-noised original electric power inspection image and the associated image; and based on the information missing degree, the artifact introduction degree and the linkage observation degree, determining the de-noising degree of the de-noised original electric power inspection image, and performing enhancement processing on the original electric power inspection image according to the de-noising degree. By adopting the method, the reliability of image enhancement processing can be improved.

The invention relates to the technical field of image processing, in particular to a fisheye lens image pixel-level edge enhancement and denoising method, which comprises the following steps: a data acquisition step: acquiring original fisheye image data and preset fisheye lens optical distortion parameters; a weight generation step: determining the distortion stretch rate of the pixel points according to the optical distortion parameters; generating a spatial density weight map; a threshold determination and denoising step: determining a target denoising threshold according to the spatial density weight map; de-noising the original fisheye image data to generate an intermediate de-noised image; a convolution kernel generation step: generating a self-adaptive curved surface convolution kernel according to the optical distortion parameters; an edge enhancement step of performing edge enhancement processing on the intermediate de-noised image by using an adaptive curved surface convolution kernel; generating an enhanced image; the risk that key semantic features of the edge view field are irreversibly erased is effectively avoided, and it is ensured that high-reliability feature retention is achieved in the extreme view field.