Real-time detection of mature table grapes using ESP-YOLO network on embedded platforms

Chen, Jiaoliao; Chen, Huan; Xu, Fang; Lin, Mengnan; Zhang, Dan; Zhang, Libin

doi:10.1016/j.biosystemseng.2024.07.014

Biosystems Engineering

2024

DOI: 10.1016/j.biosystemseng.2024.07.014

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Real-time detection of mature table grapes using ESP-YOLO network on embedded platforms

Jiaoliao Chen,

Huan Chen,

Fang Xu

et al.

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Cited by 5 publications

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Maturity detection of ‘Huping’ jujube fruits in natural environment using YOLO-FHLD

Sun,

Ren,

Zhang

et al. 2024

Smart Agricultural Technology

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Maturity detection of ‘Huping’ jujube fruits in natural environment using YOLO-FHLD

Sun,

Ren,

Zhang

et al. 2024

Smart Agricultural Technology

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LES-YOLO: efficient object detection algorithm used on UAV for traffic monitoring

Zhang,

Deng,

Lin

et al. 2024

Meas. Sci. Technol.

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The use of UAVs for traffic monitoring greatly facilitates people's lives. Classical object detection algorithms struggle to balance high speed and accuracy when processing UAV images on edge devices. To solve the problem, the paper introduces an efficient and slim YOLO with low computational overhead, named LES-YOLO. In order to enrich the feature representation of small and medium objects in UAV images, a redesigned backbone is introduced. And C2f combined with Coord Attention (CA) is used to focus on key features. In order to enrich cross-scale information and reduce feature loss during network transmission, a novel structure called EMS-PAN (Enhanced Multi-Scale PAN) is designed. At the same time, to alleviate the problem of class imbalance, Focal EIoU is used to optimize network loss calculation instead of CIoU. To minimize redundancy and ensure a slim architecture, the P5 layer has been eliminated from the model. And verification experiments show that LES-YOLO without P5 is more efficient and slimmer. LES-YOLO is trained and tested on the VisDrone2019 dataset. Compared with YOLOv8n-p2, mAP@0.5 and Recall has increased by 7.4% and 7%. The number of parameters is reduced by over 50%, from 2.9 M to 1.4 M, but there is a certain degree of increase in FLOPS, reaching 18.8 GFLOPS. However, the overall computational overhead is still small enough. Moreover, compared with YOLOv8s-p2, both the number of parameters and FLOPS are significantly reduced, while the performance is similar. As for real-time, LES-YOLO reaches 138 fps on GPU and a maximum of 78 fps on edge devices of UAV.

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Real Time Vessel Detection Model Using Deep Learning Algorithms for Controlling a Barrier System

Folarin,

Munin-Doce,

Ferreno-Gonzalez

et al. 2024

JMSE

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This study addresses marine pollution caused by debris entering the ocean through rivers. A physical and bubble barrier system has been developed to collect debris, but an effective identification and classification system for incoming vessels is needed. This study evaluates the effectiveness of deep learning models in identifying and classifying vessels in real time. The YOLO (You Only Look Once) v5 and v8 models are evaluated for vessel detection and classification. A dataset of 624 images representing 13 different types of vessels was created to train the models. The YOLOv8, featuring a new backbone network, outperformed the YOLOv5 model, achieving a high mean average precision (mAP@50) of 98.9% and an F1 score of 91.6%. However, YOLOv8’s GPU consumption increased by 116% compared to YOLOv5. The advantage of the proposed method is evident in the precision–confidence curve (PCC), where the accuracy peaks at 1.00 and 0.937 confidence, and in the achieved frames per second (fps) value of 84.7. These findings have significant implications for the development and deployment of real-time marine pollution control technologies. This study demonstrates that YOLOv8, with its advanced backbone network, significantly improves vessel detection and classification performance over YOLOv5, albeit with higher GPU consumption. The high accuracy and efficiency of YOLOv8 make it a promising candidate for integration into marine pollution control systems, enabling real-time identification and monitoring of vessels. This advancement is crucial for enhancing the effectiveness of debris collection systems and mitigating marine pollution, highlighting the potential for deep learning models to contribute to environmental preservation efforts.

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Real-time detection of mature table grapes using ESP-YOLO network on embedded platforms

Cited by 5 publications

References 31 publications

Maturity detection of ‘Huping’ jujube fruits in natural environment using YOLO-FHLD

Maturity detection of ‘Huping’ jujube fruits in natural environment using YOLO-FHLD

LES-YOLO: efficient object detection algorithm used on UAV for traffic monitoring

Real Time Vessel Detection Model Using Deep Learning Algorithms for Controlling a Barrier System

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