Method for evaluating quality of image shot by unmanned aerial vehicle

Abstract

The invention discloses a method for evaluating the quality of an image shot by an unmanned aerial vehicle, which is characterized in that for an image shot by the unmanned aerial vehicle in patrol inspection, the quality of the image is analyzed from the whole image and local details; for blurring generated by focusing misalignment caused by motions or shakes of the unmanned aerial vehicle, a frequency domain sharpness index and the distribution density and orientation features of edge segments in a space domain are firstly fused so as to judge whether an image captured by the unmanned aerial vehicle image is blur or not on the whole; under the condition that the image quality is acceptable on the whole, salient edge regions in the image are further searched, and the local detail blur degree of image edges is judged through analyzing the average width of difference salient edges. The method is applied to images shot by the unmanned aerial vehicle under outdoor conditions, the image quality can be effectively evaluated under various complex backgrounds without being influenced by the image content, and the evaluation is consistent with subjective evaluation of people in grade.

Description

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AI Technical Summary

Benefits of technology

This technology uses both the entire region around each pixel within images or areas inside them to analyze it separately from other parts like surrounding pixels. It then ranks these regions based on their importance towards overall visual performance such as brightness contrast ratio and color balance. By analyzing this data over time, we aimed at finding specific areas where there are no important details about what they're doing well without any complicated texture patterns or lights affecting how good things look when taken by robots flying through airspace.

Problems solved by technology

This patented technical problem addressed in this patents relates to accurately identifying objects within moving imagery without causing confusion due to complicated background patterns. Existing algorithms used for object recognition involve binary pixel values alone, making them difficult to interpret even if the surrounding areas contain significant detail. Additionally, existing approaches require multiple steps and take longer periods of time compared to automated systems like cameras.

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