Research on Improved Road Visual Navigation Recognition Method Based on DeepLabV3+ in Pitaya Orchard

Agronomy, Journal Year: 2024, Volume and Issue: 14(6), P. 1119 - 1119

Published: May 24, 2024

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for orchards. Initially, utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pooling (ASPP) module. improvement enhances spatial representation of maps, thereby sharpening boundaries. Additionally, Efficient Channel Network (ECANet) mechanism integrated lower-level features to reduce complexity refine clarity target The also designs algorithm, fits road mask regions segmented model achieve precise recognition. Experimental findings show that enhanced achieved Mean Intersection over Union (MIoU) average pixel accuracy 95.79% 97.81%, respectively. These figures represent increases 0.59 0.41 percentage points when contrasted original model. Furthermore, model’s consumption reduced 85.64%, 84.70%, 85.06% Scene Parsing (PSPNet), U-Net, Fully Convolutional (FCN) models, reduction makes proposed more efficient while maintaining accuracy, thus supporting operational efficiency practical applications. test results reveal angle error between centerline extracted using least squares manually fitted less than 5°. deviation centerlines under three different lighting conditions actual only 2.66 pixels, time 0.10 s. performance suggests study can provide effective reference smart agriculture.

Language: Английский

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An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: June 26, 2023

An improved YOLOv5s model was proposed and validated on a new fruit dataset to solve the real-time detection task in complex environment. With incorporation of feature concatenation an attention mechanism into original network, recorded 122 layers, 4.4 × 106 params, 12.8 GFLOPs, 8.8 MB weight size, which are 45.5%, 30.2%, 14.1%, 31.3% smaller than YOLOv5s, respectively. Meanwhile, obtained 93.4% mAP tested valid set, 96.0% test 74 fps speed videos using is 0.6%, 0.5%, 10.4% higher model, Using videos, tracking counting observed less missed incorrect detections compared YOLOv5s. Furthermore, aggregated performance outperformed network GhostYOLOv5s, YOLOv4-tiny, YOLOv7-tiny, including other mainstream YOLO variants. Therefore, lightweight with reduced computation costs, can better generalize against conditions, applicable for picking robots low-power devices.

Language: Английский

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Design and experiment of Panax notoginseng root orientation transplanting device based on YOLOv5s

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: March 8, 2024

Consistent root orientation is one of the important requirements Panax notoginseng transplanting agronomy. In this paper, a method based on machine vision technology and negative pressure adsorption principle was proposed. With cut-main roots as detection object, YOLOv5s used to establish feature model. A device designed. The control system identifies posture according results controls actuator adjust posture. show that precision rate model 94.2%, recall 92.0%, average 94.9%. Box-Behnken experiments were performed investigate effects suction plate rotation speed, servo speed angle between camera actuator(ACOA) qualification drop rate. Response surface objective optimisation algorithm analyse experimental results. optimal working parameters 5.73 r/min, 0.86 r/s ACOA 35°. Under condition, actual experiment 89.87% 6.57%, respectively, which met for roots. research paper helpful solve problem other crops.

Language: Английский

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Designing and Testing a Picking and Selecting Integrated Remote-Operation-Type Dragon-Fruit-Picking Device

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(11), P. 4786 - 4786

Published: May 31, 2024

In order to effectively solve the problems of complex growth state dragon fruit and how picking process is mostly manual, this study designed a selecting integrated remote-operation-type dragon-fruit-picking device. Based on SOLIDWORKS 2020 software for three-dimensional digital design overall assembly key components, structure working theory machine are introduced. By improving high-recognition-rate target detection algorithm based YOLOv5, better recognition locating effects were achieved targets with small size high density, as well those in bright-light scenes. Serial communication, information acquisition, precise control each action realized by building hardware platforms device system. analyzing principle mechanical system mechanics process, critical factors affecting net rate damage confirmed. force parameter analysis test results, it was confirmed that had an optimal influence when flexible claw closing speed 0.029 m/s, electric cylinder extending 0.085 arm moving 0.15 m/s. The reached 90.5%, 2.9%. can complete single fruit, plurality fruits grown at growing point, integrates integration fruits, removing bad sorting which improve efficiency harvesting replace manual work.

Language: Английский

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Jujube Fruit Instance Segmentation Based on Yolov8 Method

Published: Jan. 1, 2023

Fruit instance segmentation algorithm is necessary to consolidate fruit detection. This ensured proper area estimation of targets in an image. At the same time, high computation cost, unfriendly deployment on low-power computing devices, low detection performance, including complex environment among others are some limitations experienced by segmentation. Thus, YOLOseg-Jujube was designed based YOLOv8, and validated jujube image dataset with bounding polygons solve these problems. The architecture determined through ablation studies determine most suitable architecture. network consisted Focus, CBS, Conv4cat, SPD, SPPFr as backbone network, YOLOv8 head SIoU loss network. obtained params 62.8%, 9.8%, 73.9%, 23.6% less than YOLOv4-tiny, YOLOv5n, YOLOv7-tiny YOLOv8n, respectively. For having 83.5% B_mAP, 83.2% S_mAP, 323 fps computer 26.42 mobile phone, outperformed YOLO-mainstream variants, segmented targets. Hence, robust, fast, accurate, able identify ripeness stages, cost accessible for real-time power device applications.

Language: Английский

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Dense Papaya Target Detection in Natural Environment Based on Improved YOLOv5s

Agronomy, Journal Year: 2023, Volume and Issue: 13(8), P. 2019 - 2019

Published: July 29, 2023

Due to the fact that green features of papaya skin are same colour as leaves, dense growth fruits causes serious overlapping occlusion phenomenon between them, which increases difficulty target detection by robot during picking process. This study proposes an improved YOLOv5s-Papaya deep convolutional neural network for achieving multitarget in natural orchard environments. The model is based on YOLOv5s architecture and incorporates Ghost module enhance its lightweight characteristics. employs a strategy grouped layers weighted fusion, allowing more efficient feature representation performance. A coordinate attention introduced improve accuracy identifying papayas. fusion bidirectional pyramid networks PANet structure layer enhances performance at different scales. Moreover, scaled intersection over union bounding box regression loss function used rather than complete localisation targets expedite convergence training. Experimental results show achieves average precision, recall rates 92.3%, 90.4%, 83.4%, respectively. model’s size, number parameters, floating-point operations 11.5 MB, 6.2 M, 12.8 G, Compared original model, precision 3.6 percentage points, 4.3 parameters reduced 11.4%, decreased 18.9%. has lighter better provides theoretical basis technical support intelligent recognition occluded papayas

Language: Английский

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A simplified network topology for fruit detection, counting and mobile-phone deployment

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(10), P. e0292600 - e0292600

Published: Oct. 9, 2023

The complex network topology, deployment unfriendliness, computation cost, and large parameters, including the natural changeable environment are challenges faced by fruit detection. Thus, a Simplified topology for detection, tracking counting was designed to solve these problems. used common networks of Conv, Maxpool, feature concatenation SPPF as new backbone modified decoupled head YOLOv8 network. At same time, it validated on dataset images encompassing strawberry, jujube, cherry fruits. Having compared YOLO-mainstream variants, params is 32.6%, 127%, 50.0% lower than YOLOv5n, YOLOv7-tiny, YOLOv8n, respectively. results mAP@50% tested using test-set show that 82.4% 0.4%, -0.2%, 0.2% respectively more accurate 82.0% 82.6% 82.2% YOLOv8n. Furthermore, 12.8%, 17.8%, 11.8% faster outperforming in tracking, counting, mobile-phone process. Hence, robust, fast, accurate, easy-to-understand, fewer parameters deployable friendly.

Language: Английский

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Improving insulator fault detection with effective-YOLOv7 network

Journal of Electronic Imaging, Journal Year: 2023, Volume and Issue: 32(06)

Published: Nov. 30, 2023

Ensuring reliable and steady operation of power equipment is paramount to safeguard the livelihoods labor populace. However, traditional detection techniques face obstacles when adapting intricate background transmission lines, leading innumerable incorrect missed detections. To resolve these issues, an improved YOLOv7 line insulator defect recognition algorithm has been introduced. An anchor frame, matching size fault, constructed using K-means++, followed by a convolutional block attention module enhance ability extract features. Next, wise-IoU loss function incorporated, providing gradient gain allocation strategy, thereby enhancing positioning performance speed model. Finally, SiLU activation replaced with meta-ACON adaptive feature capability network. Experimental results have shown that proposed method average accuracy (mAP) 91.8%, for lines can be 98.8%. This model resolves persisting issues erroneous missing detections addressing technical difficulties detecting complex backgrounds defects insufficient accuracy.

Language: Английский

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A lightweight neural network approach for identifying geographical origins and predicting nutrient contents of dried wolfberries based on hyperspectral data

Journal of Food Measurement & Characterization, Journal Year: 2024, Volume and Issue: 18(9), P. 7519 - 7532

Published: July 17, 2024

Language: Английский

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Algorithm for Crop Disease Detection Based on Channel Attention Mechanism and Lightweight Up-Sampling Operator

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 109886 - 109899

Published: Jan. 1, 2024

Crop diseases and pests cause significant economic losses to agriculture every year, making accurate identification crucial. Traditional pest disease detection relies on farm experts, which is often time-consuming. Computer vision technology artificial intelligence can provide automated detection, enabling real-time precise control of crop timely prevention measures. To accurately identify plant under complex natural conditions, we developed an improved recognition model based the original YOLOv5 network. First, integrated Squeeze-and-Excitation (SE) module into YOLOv5, allowing our proposed better distinguish leaf features different crops types. Second, enhance model's feature extraction capability for diseased areas reduce loss information, replaced Up-sample in with a lightweight up-sampling operator, CARAFE module. Third, function using EIoU increase accuracy. Lastly, complexity meet requirements, introduced Ghost Convolution backbone During experimental phase, validate effectiveness, randomly divided sample images from constructed database training, validation, test sets. Experimental results showed that achieved accuracy 90.0%, recall rate 91.4%, [email protected] 92.1%, [email protected]:.95 64%. The parameter count computational load were reduced by 23.9% 31.2%, respectively, outperforming popular methods including YOLOv7, YOLOv8. conditions suitable deployment real-world applications, providing technical reference management.

Language: Английский

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