AOD-Net:面向农业自动化的轻量级水果实时检测算法

IF 2.9 3区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
Juntao Tong
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引用次数: 0

摘要

果实缺陷分类和质量目视检测是实现农业自动化收获的关键。针对现有检测模型中存在的模型参数大、目标识别精度低以及受背景噪声干扰等问题,提出了一种新的水果检测算法AOD-Net,该算法将极化自注意(PSA)机制与一种新的轻量级结构——跨阶段部分卷积(CSPC)相结合。AOD-Net通过在骨干网末端加入PSA模块,建立了特征通道之间的相互依赖关系,降低了背景噪声,提高了网络对目标细微特征的提取和识别能力,从而提高了目标的定位精度。CSPC结构受到Dual-Conv和Partial-Conv的启发,取代了某些卷积层,显著减少了模型参数,加快了检测速度,同时保持了精度,满足了实时要求。在颈部网络中加入接收场注意卷积模块,增强特征学习,提高特征提取精度,解决参数共享问题,从而提高模型泛化能力。此外,dyssample上采样算子取代了传统的最近邻插值,减少了计算参数,同时改善了不同水果类型的特征融合,从而提高了检测精度和鲁棒性。在公开的FruitNet数据集上的实验结果表明,AOD-Net的mAP达到了93.55%,与标准的yolov5相比,Precision、Recall和mAP分别提高了1.30%、1.96%和3.95%。模型的内存使用率降低了8.97%,计算成本从16.0 GFLOPs降低到11.3 GFLOPs,验证了算法的有效性。AOD-Net在速度和准确性之间取得了很好的平衡,使其成为一种高效实用的水果检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AOD-Net: a lightweight real-time fruit detection algorithm for agricultural automation

Fruit defect classification and quality visual inspection are crucial for automated harvesting in agriculture. To address the issues of large model parameters, low target recognition accuracy, and interference from background noise in existing detection models, we proposed a novel fruit detection algorithm, AOD-Net, which integrated Polarized self-attention (PSA) mechanism and a new lightweight structure, Cross-Stage Partial Convolution (CSPC). By adding the PSA module at the end of the backbone network, AOD-Net establishes mutual dependencies between feature channels, reducing background noise and improving the network’s ability to extract and recognize subtle target features, thus enhancing target localization accuracy. The CSPC structure, inspired by Dual-Conv and Partial-Conv, replaces certain convolutional layers, significantly reducing model parameters and accelerating detection speed while maintaining accuracy to meet real-time requirements. The Receptive-Field Attention Convolution module is incorporated into the neck network to enhance feature learning, improve feature extraction accuracy, and address parameter sharing issues, thus improving model generalization. Additionally, the Dysample upsampling operator replaces the traditional nearest-neighbor interpolation to reduce computational parameters while improving feature fusion for different fruit types, thereby enhancing detection accuracy and robustness. Experimental results on the publicly available FruitNet dataset showed that AOD-Net achieved a mAP of 93.55%, with improvements of 1.30%, 1.96%, and 3.95% in Precision, Recall, and mAP, respectively, compared to the standard YOLOv5s. The model’s memory usage decreased by 8.97%, and the computational cost was reduced from 16.0 GFLOPs to 11.3 GFLOPs, verifying the effectiveness of the proposed algorithm. AOD-Net strikes an excellent balance between speed and accuracy, making it an efficient and practical fruit detection method.

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来源期刊
Journal of Food Measurement and Characterization
Journal of Food Measurement and Characterization Agricultural and Biological Sciences-Food Science
CiteScore
6.00
自引率
11.80%
发文量
425
期刊介绍: This interdisciplinary journal publishes new measurement results, characteristic properties, differentiating patterns, measurement methods and procedures for such purposes as food process innovation, product development, quality control, and safety assurance. The journal encompasses all topics related to food property measurement and characterization, including all types of measured properties of food and food materials, features and patterns, measurement principles and techniques, development and evaluation of technologies, novel uses and applications, and industrial implementation of systems and procedures.
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