DCFA-YOLO: A Dual-Channel Cross-Feature-Fusion Attention YOLO Network for Cherry Tomato Bunch Detection

Shanglei Chai, Ming Wen, Pengyu Li, Zhi Zeng and Yibin Tian ()
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Shanglei Chai: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Ming Wen: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Pengyu Li: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Zhi Zeng: School of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
Yibin Tian: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China

Agriculture, 2025, vol. 15, issue 3, 1-19

Abstract: To better utilize multimodal information for agriculture applications, this paper proposes a cherry tomato bunch detection network using dual-channel cross-feature fusion. It aims to improve detection performance by employing the complementary information of color and depth images. Using the existing YOLOv8_n as the baseline framework, it incorporates a dual-channel cross-fusion attention mechanism for multimodal feature extraction and fusion. In the backbone network, a ShuffleNetV2 unit is adopted to optimize the efficiency of initial feature extraction. During the feature fusion stage, two modules are introduced by using re-parameterization, dynamic weighting, and efficient concatenation to strengthen the representation of multimodal information. Meanwhile, the CBAM mechanism is integrated at different feature extraction stages, combined with the improved SPPF_CBAM module, to effectively enhance the focus and representation of critical features. Experimental results using a dataset obtained from a commercial greenhouse demonstrate that DCFA-YOLO excels in cherry tomato bunch detection, achieving an mAP50 of 96.5%, a significant improvement over the baseline model, while drastically reducing computational complexity. Furthermore, comparisons with other state-of-the-art YOLO and other object detection models validate its detection performance. This provides an efficient solution for multimodal fusion for real-time fruit detection in the context of robotic harvesting, running at 52fps on a regular computer.

Keywords: cherry tomato bunch detection; robotic harvesting; multimodal image; feature extraction; feature fusion; YOLO 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: 2025
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