首页|Jiangsu University Reports Findings in Robotics (Green apple detection method ba sed on multi-dimensional feature extraction network model and Transformer module )
Jiangsu University Reports Findings in Robotics (Green apple detection method ba sed on multi-dimensional feature extraction network model and Transformer module )
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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.”
ZhenjiangPeople’s Republic of ChinaA siaEmerging TechnologiesFood SafetyMachine LearningRobotRobotics
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