Wang Jinming, Alshahir Ahmed, Abbas Ghulam, Kaaniche Khaled, Albekairi Mohammed, Alshahr Shahr, Aljarallah Waleed, Sahbani Anis, Nowakowski Grzegorz, Sieja Marek
College of Information Science & Technology, Zhejiang Shuren University, Hangzhou 310015, China.
Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia.
Sensors (Basel). 2023 Aug 31;23(17):7556. doi: 10.3390/s23177556.
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.
在高对比度、多目标图像和视频中进行目标检测具有挑战性。这种困难源于不同区域以及物体/人物具有不同的像素分布、对比度和强度属性。这项工作引入了一种新的区域聚焦特征检测（RFD）方法来解决这个问题并提高目标检测精度。RFD方法将输入图像划分为几个较小的图像，以便尽可能多地处理图像。计算这些区域中每个区域的对比度和强度属性。然后使用深度循环学习，通过与对应于各个区域的训练输入的相似性度量来迭代提取这些特征。通过组合来自多个重叠位置的特征可以定位目标。借助对比度和强度属性，将识别出的目标与训练期间使用的输入进行比较，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。该方法在区域选择和与众多提取实例的模式匹配过程中有效地降低了误报率。因此，所提出的方法通过挑选出不同区域并滤除产生误导率的特征来提供更高的准确性。使用准确率、相似性指数、误报率、提取率、处理时间等指标来评估所提出方法的有效性。所提出的RFD将相似性指数提高了10.69%，提取率提高了9.04%，精度提高了13.27%。误报率和处理时间分别降低了7.78%和9.19%。
