CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement

Author:

Liu Jiahao1,He Chaoying1,Wang Mingfang1,Jiang Yichu2,Sun Manman3,Yan Miying1,He Mingfang1

Affiliation:

1. College of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China

2. Hunan Polytechnic of Environment and Biology, Hengyang 421005, China

3. Bangor College China, Central South University of Forestry and Technology, Changsha 410004, China

Abstract

Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer from light and noise in scenarios with dense small target clusters and extreme light. To solve these problems, this study proposes a multimodal detector, called CRLNet, based on RGB and depth images. First, YOLOv9 was extended to design a backbone network that can extract RGB and depth features in parallel from an image. Second, to address the problem of information fusion bias, the Rough–Fine Hybrid Attention Fusion Module (RFAM) was designed to combine the advantageous information of different modes while suppressing the hollow noise at the edge of the peach. Finally, a Transformer-based Local–Global Joint Enhancement Module (LGEM) was developed to jointly enhance the local and global features of peaches using information from different modalities in order to enhance the percentage of information about the target peaches and remove the interference of redundant background information. CRLNet was trained on the Peach dataset and evaluated against other state-of-the-art methods; the model achieved an mAP50 of 97.1%. In addition, CRLNet also achieved an mAP50 of 92.4% in generalized experiments, validating its strong generalization capability. These results provide valuable insights for peach and other outdoor fruit multimodal detection.

Funder

Scientific Research Project of Education Department of Hunan Province

Natural Science Foundation of China

Hunan Provincial Natural Science Foundation of China

Publisher

MDPI AG

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