Long-Strip Target Detection and Tracking with Autonomous Surface Vehicle

Zhang, Meiyang; Zhao, Dongyang; Sheng, Cailiang; Liu, Ziqiang; Cai, Wenyu

doi:10.3390/jmse11010106

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Zhang et al [7] proposed a method of accurate target detection with long-strip targets on the water, based on a convolutional neural network, for detecting and tracking targets in the processes of sea exploration and protection. Using a dicyclic loop PID control scheme, the autonomous surface vehicle is steered to approach a long-strip target with a near-optimal path design.…”

Section: State Of Knowledgementioning

confidence: 99%

Optimality of Safe Game and Non-Game Control of Marine Objects

Lisowski

2023

Electronics

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The article presents a model of the process of safe and optimal control of an autonomous surface object in a group of encountered objects. An algorithm for determining the optimal and safe trajectory based on a multi-object game model was proposed, and an algorithm for determining the optimal trajectory was proposed for comparative analysis, not taking into account the maneuverability of other objects. Simulation studies of the algorithms made it possible to assess the optimality of the trajectories for various acceptable object strategies. An analysis of the characteristics of the sensitivity of the safe control—assessed with the risk of collision, both on the inaccuracy of navigation data and on the number of possible strategies of objects, was carried out.

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Section: State Of Knowledgementioning

confidence: 99%

Optimality of Safe Game and Non-Game Control of Marine Objects

Lisowski

2023

Electronics

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“…Zhang et al [7] proposed a method of accurate target detection with long-strip targets on the water, based on a convolutional neural network, for detecting and tracking targets in the processes of sea exploration and protection. The closed control system with a PID controller ensures its optimal approximation to the longitudinal target.…”

Section: Of 14mentioning

confidence: 99%

Optimality Comparison of Object Safe Trajectory in a Multi-Object Passing Situation for Different Quantities of Acceptable Game and Non-Game Control Strategies

Lisowski¹

2023

Preprint

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This paper presents an analysis of the sensitivity of controlling an autonomous surface object among a group of encountered objects to the inaccuracy of state control process data. For this purpose, a safe and optimal model of the control process was defined. An algorithm for determining the optimal and safe trajectory of an object, based on the multi-object game model, was developed; for comparative analysis, an algorithm for calculating only the optimal trajectory, not taking into account the maneuverability of other objects, was additionally developed. First, simulation-based studies of the algorithms enabled a comparative analysis of a number of different acceptable strategies for optimally shifting the trajectory of maneuvering objects from a single initial direction. Thereafter, the main goal of this paper was implemented: an analysis of the sensitivity characteristics of safe control, assessed with the risk of collision, both on the inaccuracy of navigation data and on the number of possible control strategies. Finally, final conclusions and a plan for further research on the subject of the paper were formulated.

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“…Currently, computer vision technology is widely used in agriculture and has made great progress in the accuracy and efficiency of extracting plant phenotypic data. Currently, there are two main detection methods for obtaining plant phenotypic data: traditional target detection methods and target detection methods based on deep learning ( Zhang et al., 2023 ). Among them, the traditional target detection process is more complex, requiring multiple steps to be completed together and time-consuming, with higher requirements for images, different algorithms for different detection objects, and greater difficulty in extracting different information at the same time; deep learning has a powerful feature extraction capability, which can make up for the shortcomings of the traditional methods, and therefore, more and more researchers are using it for agricultural target detection.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic detection of flax plants based on improved Flax-YOLOv5

Sun,

Liu,

Han

et al. 2024

Front. Plant Sci.

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Accurate detection and counting of flax plant organs are crucial for obtaining phenotypic data and are the cornerstone of flax variety selection and management strategies. In this study, a Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data. Based on the solid foundation of the original YOLOv5x feature extraction network, the network structure was extended to include the BiFormer module, which seamlessly integrates bi-directional encoders and converters, enabling it to focus on key features in an adaptive query manner. As a result, this improves the computational performance and efficiency of the model. In addition, we introduced the SIoU function to compute the regression loss, which effectively solves the problem of mismatch between predicted and actual frames. The flax plants grown in Lanzhou were collected to produce the training, validation, and test sets, and the detection results on the validation set showed that the average accuracy (mAP@0.5) was 99.29%. In the test set, the correlation coefficients (R) of the model’s prediction results with the manually measured number of flax fruits, plant height, main stem length, and number of main stem divisions were 99.59%, 99.53%, 99.05%, and 92.82%, respectively. This study provides a stable and reliable method for the detection and quantification of flax phenotypic characteristics. It opens up a new technical way of selecting and breeding good varieties.

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Long-Strip Target Detection and Tracking with Autonomous Surface Vehicle

Cited by 7 publications

References 25 publications

Optimality of Safe Game and Non-Game Control of Marine Objects

Optimality of Safe Game and Non-Game Control of Marine Objects

Optimality Comparison of Object Safe Trajectory in a Multi-Object Passing Situation for Different Quantities of Acceptable Game and Non-Game Control Strategies

Phenotypic detection of flax plants based on improved Flax-YOLOv5

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