Green Apple Detector Based on Optimized Deformable Detection Transformer

Qiaolian Liu, Hu Meng, Ruina Zhao, Xiaohui Ma, Ting Zhang () and Weikuan Jia ()
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Qiaolian Liu: School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China
Hu Meng: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Ruina Zhao: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Xiaohui Ma: School of Computer Science and Technology, Shandong University, Qingdao 266237, China
Ting Zhang: School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China
Weikuan Jia: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Agriculture, 2024, vol. 15, issue 1, 1-18

Abstract: In the process of smart orchard construction, accurate detection of target fruit is an important guarantee to realize intelligent management of orchards. Green apple detection technology greatly diminishes the need for manual labor, cutting costs and time, while enhancing the automation and efficiency of sorting processes. However, due to the complex orchard environment, the ever-changing posture of the target fruit, and the difficulty of detecting green target fruit similar to the background, they bring new challenges to the detection of green target fruit. Aiming at the problems existing in green apple detection, this study takes green apples as the research object, and proposes a green apple detection model based on optimized deformable DETR. The new method first introduces the ResNeXt network to extract image features to reduce information loss in the feature extraction process; secondly, it improves the accuracy and optimizes the detection results through the deformable attention mechanism; and finally, it uses a feed-forward network to predict the detection results. The experimental results show that the accuracy of the improved detection model has been significantly improved, with an overall AP of 54.1, AP 50 of 80.4, AP 75 of 58.0, AP s of 35.4 for small objects, AP m of 60.2 for medium objects, and AP l of 85.0 for large objects. It can provide a theoretical reference for green target detection of other fruit and vegetables green target detection.

Keywords: green apples; object detection; deformable attention; deformable DETR network; ResNeXt network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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