Real-time vehicle identification and tracking during agricultural master-slave follow-up operation using improved YOLO v4 and binocular positioning

Wang, Liang; Li, Lingmin; Wang, Hao; Zhu, Shaohua; Zhai, Zhiqiang; Zhu, Zhen

doi:10.1177/09544062221130928

Cited by 10 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This integration facilitates high-accuracy, real-time row detection, laying the groundwork for precise extraction of navigation lines from crop images. Efforts to refine one-stage detection methods have led to significant advancements in detection accuracy, enabling the development of innovative solutions such as the “LettuceTrack” by ( Hu et al., 2021 ), which employs the YOLOv5 network model for vegetable detection and tracking, and the real-time vehicle recognition and tracking method proposed by ( Wang L. et al., 2023 ), leveraging an improved YOLOv4 model ( Yang et al., 2022 ). utilized the YOLOv3 network model to fit seedling crop rows based on detection results.…”

Section: Introductionmentioning

confidence: 99%

Improving the maize crop row navigation line recognition method of YOLOX

Gong,

Zhuang,

Wang

2024

Front. Plant Sci.

View full text Add to dashboard Cite

The accurate identification of maize crop row navigation lines is crucial for the navigation of intelligent weeding machinery, yet it faces significant challenges due to lighting variations and complex environments. This study proposes an optimized version of the YOLOX-Tiny single-stage detection network model for accurately identifying maize crop row navigation lines. It incorporates adaptive illumination adjustment and multi-scale prediction to enhance dense target detection. Visual attention mechanisms, including Efficient Channel Attention and Cooperative Attention modules, are introduced to better extract maize features. A Fast Spatial Pyramid Pooling module is incorporated to improve target localization accuracy. The Coordinate Intersection over Union loss function is used to further enhance detection accuracy. Experimental results demonstrate that the improved YOLOX-Tiny model achieves an average precision of 92.2 %, with a detection time of 15.6 milliseconds. This represents a 16.4 % improvement over the original model while maintaining high accuracy. The proposed model has a reduced size of 18.6 MB, representing a 7.1 % reduction. It also incorporates the least squares method for accurately fitting crop rows. The model showcases efficiency in processing large amounts of data, achieving a comprehensive fitting time of 42 milliseconds and an average angular error of 0.59°. The improved YOLOX-Tiny model offers substantial support for the navigation of intelligent weeding machinery in practical applications, contributing to increased agricultural productivity and reduced usage of chemical herbicides.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving the maize crop row navigation line recognition method of YOLOX

Gong,

Zhuang,

Wang

2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…For example, Wu et al [32] used a modified YOLO model to detect how pine nematode disease affected trees at different stages of infection. Wang et al [33] used improved YOLO-V4 and binocular positioning for real-time vehicle identification and tracking during an agricultural operation. Qiu et al [34] used a YOLO-based method to detect sidewalk cracks in real-time drone images.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Detection Model of Takifugu rubripes Using an Improved YOLO-V7 Network

Zhou

Cai

Yang

et al. 2023

JMSE

View full text Add to dashboard Cite

In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. However, the underwater environment is complex and affected by lighting, water quality, and the mutual obscuration of fish bodies. Therefore, underwater fish images are not very clear, which restricts the recognition accuracy of underwater targets. This paper proposes an improved YOLO-V7 model for the identification of Takifugu rubripes. Its specific implementation methods are as follows: (1) The feature extraction capability of the original network is improved by adding a sizeable convolutional kernel model into the backbone network. (2) Through ameliorating the original detection head, the information flow forms a cascade effect to effectively solve the multi-scale problems and inadequate information extraction of small targets. (3) Finally, this paper appropriately prunes the network to reduce the total computation of the model; meanwhile, it ensures the precision of the detection. The experimental results show that the detection accuracy of the improved YOLO-V7 model is better than that of the original. The average precision improved from 87.79% to 92.86% (when the intersection over union was 0.5), with an increase of 5.07%. Additionally, the amount of computation was reduced by approximately 35%. This shows that the detection precision of the proposed network model was higher than that for the original model, which can provide a reference for the intelligent aquaculture of fishes.

show abstract

“…At present, convolutional neural networks in deep learning are widely used in text, speech, picture, and video [13][14][15]. They show great advantages, especially in target detection tasks, as they can quickly and accurately complete the detection task [16][17][18]. To solve the recognition and localization of steering markers in complex scenarios in orchards, the seventh-generation algorithm YOLOv7 of the regression-based YOLO series was selected for steering mark recognition, and a binocular camera was used as the vision sensor for steering mark localization.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Rapid Detection of Steering Markers in Orchard Management Robots Based on Improved YOLOv7

Gao,

Tian,

et al. 2023

Electronics

View full text Add to dashboard Cite

In order to guide the orchard management robot to realize autonomous steering in the row ends of a complex orchard environment, this paper proposes setting up steering markers in the form of fruit trees at the end of the orchard rows and realizing the rapid detection of the steering markers of the orchard management robot through the fast and accurate recognition and classification of different steering markers. First, a high-precision YOLOv7 model is used, and the depthwise separable convolution (DSC) is used instead of the 3 × 3 ordinary convolution, which improves the speed of model detection; at the same time, in order to avoid a decline in detection accuracy, the Convolutional Block Attention Module (CBAM) is added to the model, and the Focal loss function is introduced to improve the model’s attention to the imbalanced samples. Second, a binocular camera is used to quickly detect the steering markers, obtain the position information of the robot to the steering markers, and determine the starting point position of the robot’s autonomous steering based on the position information. Our experiments show that the average detection accuracy of the improved YOLOv7 model reaches 96.85%, the detection time of a single image reaches 15.47 ms, and the mean value of the localization error is 0.046 m. Comparing with the YOLOv4, YOLOv4-tiny, YOLOv5-s, and YOLOv7 models, the improved YOLOv7 model outperforms the other models in terms of combined detection time and detection accuracy. Therefore, the model proposed in this paper can quickly and accurately perform steering marker detection and steering start point localization, avoiding problems such as steering errors and untimely steering, shortening the working time and improving the working efficiency. This model also provides a reference and technical support for research on robot autonomous steering in other scenarios.

show abstract

Real-time vehicle identification and tracking during agricultural master-slave follow-up operation using improved YOLO v4 and binocular positioning

Cited by 10 publications

References 23 publications

Improving the maize crop row navigation line recognition method of YOLOX

Improving the maize crop row navigation line recognition method of YOLOX

An Accurate Detection Model of Takifugu rubripes Using an Improved YOLO-V7 Network

A Study on the Rapid Detection of Steering Markers in Orchard Management Robots Based on Improved YOLOv7

Contact Info

Product

Resources

About