摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器人的新研究是一份报告的结尾。根据新闻报道来自中华人民共和国镇江，由NewsRx记者报道，研究称：“至”为了食品安全,提高绿色苹果的快速、准确检测以dtr网络为框架,提出一种新的绿色苹果无损检测方法一个多维特征提取网络和变压器模块。首先，改进的DETR网络主特征提取模块采用ResNet18网络,并替换部分剩余层采用Defor Mable Convolutions(DCNv2)，使模型更好地适应无公害水果的变化在不同尺度和角度下，同时消除微生物污染对水果检测的影响；随后，扩展的空间pyra中间池模型(DSPP)和多尺度剩余聚集集成了模块(FRAM)，有助于降低功能噪音并最大限度地减少底层功能的损失在特征提取过程中。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Robotics is the subjec t of a report. According to news reportingoriginating from Zhenjiang, People’s Republic of China, by NewsRx correspondents, research stated, “Toenhance fast a nd accurate detection of pollution-free green apples for food safety, this paper uses theDETR network as a framework to propose a new method for pollution-free green apple detection basedon a multi-dimensional feature extraction network a nd Transformer module. Firstly, an improved DETRnetwork main feature extraction module adopts the ResNet18 network and replaces some residual layerswith defor mable convolutions (DCNv2), enabling the model to better adapt to pollution-free fruit changesat different scales and angles, while eliminating the impact of m icrobial contamination on fruit testing;Subsequently, the extended spatial pyra mid pooling model (DSPP) and multi-scale residual aggregationmodule (FRAM) are integrated, which help reduce feature noise and minimize the loss of underlying featuresduring the feature extraction process.”
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