| Title: |
RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. |
| Authors: |
Sui, Jinxue1 (AUTHOR) suijx@sdtbu.edu.cn, Liu, Li1 (AUTHOR), Wang, Zuoxun1 (AUTHOR) wangzuoxun@126.com, Yang, Li1 (AUTHOR) |
| Source: |
PLoS ONE. 3/3/2025, Vol. 20 Issue 3, p1-20. 20p. |
| Subject Terms: |
*DETECTION algorithms, *APPLE harvesting, *ROBOTICS, *PROBLEM solving, *ALGORITHMS |
| Abstract: |
The widespread cultivation of apples highlights the importance of efficient and accurate apple detection algorithms in robotic picking technology. The accuracy of current apple picking detection algorithms is still limited when the distribution is dense and occlusion exists, and there is a significant challenge in deploying current high accuracy detection models on edge devices with limited computational resources. To solve the above problems, this paper proposes an improved detection algorithm (RE-YOLO) based on YOLOv8n. First, this paper innovatively introduces Receptive-Field Attention Convolution (RFAConv) to improve the backbone and neck network of YOLOv8. It essentially solves the problem of convolution kernel parameter sharing and improves the consideration of the differential information from different locations, which significantly improves the accuracy of model recognition. Second, this paper innovatively proposes an EMA_C2f module. This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. The experimental results show that RE-YOLO improves the precision, recall, mAP@0.5, and mAP@0.5-0.95 by 2%, 2.1%, 2.7%, and 3.9%, respectively, compared with the original YOLOv8. Compared with YOLOv5, it improves 4%, 1.9%, 1.7% and 3%, respectively, which fully proves the advanced and practical nature of the proposed algorithm. [ABSTRACT FROM AUTHOR] |
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