PDF(Pubmed)
Abstract：
Target detection in high-contrast, multi-object images and movies is challenging. This difficulty results from different areas and objects/people having varying pixel distributions, contrast, and intensity properties. This work introduces a new region-focused feature detection (RFD) method to tackle this problem and improve target detection accuracy. The RFD method divides the input image into several smaller ones so that as much of the image as possible is processed. Each of these zones has its own contrast and intensity attributes computed. Deep recurrent learning is then used to iteratively extract these features using a similarity measure from training inputs corresponding to various regions. The target can be located by combining features from many locations that overlap. The recognized target is compared to the inputs used during training, with the help of contrast and intensity attributes, to increase accuracy. The feature distribution across regions is also used for repeated training of the learning paradigm. This method efficiently lowers false rates during region selection and pattern matching with numerous extraction instances. Therefore, the suggested method provides greater accuracy by singling out distinct regions and filtering out misleading rate-generating features. The accuracy, similarity index, false rate, extraction ratio, processing time, and others are used to assess the effectiveness of the proposed approach. The proposed RFD improves the similarity index by 10.69%, extraction ratio by 9.04%, and precision by 13.27%. The false rate and processing time are reduced by 7.78% and 9.19%, respectively.
摘要：
高对比度的目标检测，多目标图像和电影是具有挑战性的。这种困难是由于不同的区域和对象/人具有不同的像素分布，对比,和强度属性。这项工作引入了一种新的区域聚焦特征检测（RFD）方法来解决此问题并提高目标检测精度。RFD方法将输入图像分成几个较小的图像,以便处理尽可能多的图像。这些区域中的每一个具有其自己计算的对比度和强度属性。然后使用深度循环学习来使用相似性度量从对应于各个区域的训练输入中迭代地提取这些特征。可以通过组合来自重叠的许多位置的特征来定位目标。将识别的目标与训练期间使用的输入进行比较，在对比度和强度属性的帮助下，以提高准确性。跨区域的特征分布也用于学习范式的重复训练。该方法有效地降低了在具有大量提取实例的区域选择和模式匹配期间的错误率。因此,建议的方法通过挑出不同的区域并过滤掉误导性的速率生成特征来提供更高的准确性。准确性,相似性指数,虚假率，提取率，处理时间,和其他人被用来评估所提出的方法的有效性。提出的RFD将相似性指数提高了10.69%，提取率9.04%，精密度提高了13.27%。错误率和处理时间分别减少了7.78%和9.19%，分别。
