TransSSA: Invariant Cue Perceptual Feature Focused Learning for Dynamic Fruit Target Detection  

作　　者：Jianyin Tang Zhenglin Yu Changshun Shao 

机构地区：[1]School of Mechanical and Electrical Engineering,Changchun University of Science and Technology,Changchun,130022,China

出　　处：《Computers, Materials & Continua》2025年第5期2829-2850,共22页计算机、材料和连续体(英文)

基　　金：supported in part by the Basic Research Project of Science and Technology Department of Jilin Province,China(Grant No.202002044JC).

摘　　要：In the field of automated fruit harvesting,precise and efficient fruit target recognition and localization play a pivotal role in enhancing the efficiency of harvesting robots.However,this domain faces two core challenges:firstly,the dynamic nature of the automatic picking process requires fruit target detection algorithms to adapt to multi-view characteristics,ensuring effective recognition of the same fruit from different perspectives.Secondly,fruits in natural environments often suffer from interference factors such as overlapping,occlusion,and illumination fluctuations,which increase the difficulty of image capture and recognition.To address these challenges,this study conducted an in-depth analysis of the key features in fruit recognition and discovered that the stem,body,and base serve as constant and core information in fruit identification,exhibiting long-term dependent semantic relationships during the recognition process.These invariant features provide a stable foundation for dynamic fruit recognition,contributing to improved recognition accuracy and robustness.Specifically,the morphology and position of the stem,body,and base are relatively fixed,and the effective extraction of these features plays a crucial role in fruit recognition.This paper proposes a novel model,TransSSA,and designs two innovative modules to effectively extract fruit image features.The Self-Attention Core Feature Extraction(SAF)module integrates YOLOV8 and Swin Transformer as backbone networks and introduces the Shuffle Attention self-attention mechanism,significantly enhancing the ability to extract core features.This module focuses on constant features such as the stem,body,and base,ensuring accurate fruit recognition in different environments.On the other hand,the Squeeze and Excitation Aggregation(SAE)module combines the network’s ability to capture channel patterns with global knowledge,further optimizing the extraction of effective features.Additionally,to improve detection accuracy,this studymodifies the regression loss

关 键 词：Fruit recognition invariant features TransSSA model swin transformer self-attention mechanism 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]