在嵌入式平台上使用 ESP-YOLO 网络实时检测成熟的餐桌葡萄

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING Biosystems Engineering Pub Date : 2024-07-31 DOI:10.1016/j.biosystemseng.2024.07.014
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引用次数: 0

摘要

嵌入式平台上的实时和高精度检测方法对于收获机器人准确定位鲜食葡萄的位置至关重要。针对大棚结构果园中的鲜食葡萄,提出了一种新颖的检测方法(ESP-YOLO),基于 "只看一次"(YOLO)、高效层洗牌聚合网络(ELSAN)、挤压激励(SE)、部分卷积(PConv)和软非最大抑制(Soft_NMS),提高了检测精度和效率。根据跨组信息交换,提出了信道洗牌操作来修改过渡层,而不是主干网络中的 CSPDarkNet53 (C3),以进行餐桌葡萄特征提取。在颈部网络中利用 PConv 提取部分通道的推理速度和空间特征。在骨干网络中插入 SE,以调整通道权重,从而提取葡萄图像的通道特征。然后,对 Soft_NMS 进行修改,以增强对密集聚类葡萄的分割能力。该算法在嵌入式平台上进行,以检测复杂场景下的餐桌葡萄,包括多葡萄粘连重叠和茎叶遮挡。ELSAN 块将推理速度提高了 46%,同时保持了准确性。ESP-YOLO的[email protected]:0.95比其他先进方法的[email protected]:0.95高出3.7%-16.8%。ESP-YOLO是收获机器人在各种复杂情况下准确、快速地检测鲜食葡萄的有用工具。
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Real-time detection of mature table grapes using ESP-YOLO network on embedded platforms

The real-time and high-precision detection methods on embedded platforms are critical for harvesting robots to accurately locate the position of the table grapes. A novel detection method (ESP-YOLO) for the table grapes in the trellis structured orchards is proposed to improve the detection accuracy and efficiency based on You Only Look Once (YOLO), Efficient Layer Shuffle Aggregation Networks (ELSAN), Squeeze-and-Excitation (SE), Partial Convolution (PConv) and Soft Non-maximum suppression (Soft_NMS). According to cross-group information interchange, the channel shuffle operation is presented to modify transition layers instead of the CSPDarkNet53 (C3) in backbone networks for the table grape feature extraction. The PConv is utilised in the neck network to extract the part channel's features for the inference speed and spatial features. SE is inserted in backbone networks to adjust the channel weight for channel-wise features of grape images. Then, Soft_NMS is modified to enhance the segmentation capability for densely clustered grapes. The algorithm is conducted on embedded platforms to detect table grapes in complex scenarios, including the overlap of multi-grape adhesion and the occlusion of stems and leaves. ELSAN block boosts inference speed by 46% while maintaining accuracy. The [email protected]:0.95 of ESP-YOLO surpasses that of other advanced methods by 3.7%–16.8%. ESP-YOLO can be a useful tool for harvesting robots to detect table grapes accurately and quickly in various complex scenarios.

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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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