基于图像处理模型和数据融合的弱小目标图像视觉传达设计

Xu Zhang
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摘要

:计算机视觉技术的本质是将计算机与图像数据结合起来,提高计算机的理解和感知能力。物体识别是计算机视觉技术的一大研究热点,而在物体识别领域,弱小目标图像的识别是一大难题。针对目前检测弱小目标图像的难点,提出了一种具有图像处理模型和数据融合的视觉通信设计。改进了单发多箱检测器,构建了弱小目标检测模型,并结合特征融合方法增强了弱小目标检测能力。消融实验表明,与原始模型相比,改进后的模型对弱目标和小目标的检测能力提高了 26.54%,总体准确率提高了 11.05%。实验还选取了另外两种先进算法与研究算法进行对比,结果发现研究算法的准确性更好,准确率比对比算法高 47.67% -79.56%。响应时间更短,仅为 0.62 秒。视觉通信时间和成功率表现更好,通信时间从 9 秒领先到 19 秒,成功率从 16% 领先到 27%。综上所述,该研究所提出的算法在检测小图像和弱图像方面具有更高的准确性,以及更高的视觉通信效率和成功率,在计算机视觉领域具有更强的实用意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Communication Design of Weak and Small Target Images Based on Image Processing Model and Data Fusion
: The essence of computer vision technology is to combine computers with image data to enhance their understanding and perception abilities. One of the major research hotspots in computer vision technology is the object recognition, and in the field of object recognition, a major challenge is the recognition of weak and small target images. In response to the current difficulty in detecting weak and small target images, a visual communication design with image processing models and data fusion was proposed. The Single Shot MultiBox Detector was improved to construct a weak and small target detection model, and the feature fusion method was combined to enhance its weak and small target detection ability. The ablation experiment showed that in contrast with the original model, the improved model improved the detection ability of weak and small targets by 26.54%, and the overall accuracy improved by 11.05%. Two other advanced algorithms were selected for comparison with the research algorithm, and the accuracy of the research algorithm was better, with a higher accuracy of 47.67% -79.56% than the comparison algorithm. The response time was shorter, reaching 0.62 seconds. Visual communication time and success rate performed better, with a communication time lead of 9 s to 19 s and a success rate lead of 16% to 27%. In summary, the algorithm proposed by the research institute has higher accuracy in detecting small and weak images, as well as higher visual communication efficiency and success rate, which has stronger practical significance in the field of computer vision.
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