Green Apple Detector Based on Optimized Deformable Detection Transformer
2024
Qiaolian Liu | Hu Meng | Ruina Zhao | Xiaohui Ma | Ting Zhang | Weikuan Jia
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, AP50 of 80.4, AP75 of 58.0, APs of 35.4 for small objects, APm of 60.2 for medium objects, and APl of 85.0 for large objects. It can provide a theoretical reference for green target detection of other fruit and vegetables green target detection.
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