Research on feature extraction and segmentation of rover wheel imprint

Li, Nan; Gao, Haibo; Ding, Lixin; Lv, Fengtian; Bi, Zhong-yan; Wang, Yi-da

doi:10.1007/s11227-018-2502-7

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Step 1: Unify size of feature maps Check the global attention feature map F 1 t and local attention feature map F 2 t , conduct adaptive maximum pooling to reduce the spatial dimension to consistency, and maximum pooling can well preserve the texture information of the fruit fly images [21]; so, F 1 t and F 2 t are not only aggregates of a large amount of spatial information, but also effectively reduce the calculation amount of the deep learning model and improve the recognition accuracy of fruit fly fine-grained images [22].…”

Section: Multi-channel Self-attention Feature Fusion Of Fine-grained ...mentioning

confidence: 99%

Enhancing Fine-Grained Image Recognition with Multi-Channel Self-Attention Mechanisms: A Focus on Fruit Fly Species Classification

Lu,

Yi,

2024

Applied Sciences

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Fruit fly species classification is a fine-grained task as there is a small gap between species. In order to effectively identify and improve the recognition of fruit flies, a fine-grained image-recognition method based on a multi-channel self-attention mechanism was studied and a network framework for fine-grained image recognition based on deep learning was designed in this paper. In this framework, long-term and short-term memory networks are used to extract the underlying features in fruit fly fine-grained images. By inputting the underlying features in the multi-channel self-attention mechanism module, the global and local attention feature maps can be obtained.The weighted attention feature map can also be obtained by multiplying the weight of each channel and the attention feature map. The fine-grained image features of fruit flies were obtained by summing the weighted attention feature map. A softmax classifier was used to process the features and complete the recognition of the fruit fly fine-grained images. Two fine-grained image datasets of fruit flies were applied as experimental objects. Dataset 1 and Dataset 2 contain 11,778 images and 20,580 images from 46 different categories of fruit flies, respectively. The Kappa coefficient was used as the evaluation index to identify fruit fly images with different targets using the method proposed herein. The experimental results showed that, as the number of attention channels increased, the Kappa coefficient gradually increased, suggesting an improvement in the accuracy of fine-grained image recognition. The fine-grained image features extracted by introducing a multi-channel self-attention mechanism exhibited more distinct boundaries with a small amount of overlap, demonstrating strong feature extraction capabilities. When dealing with fine-grained images with either simple or complex backgrounds, the method proposed in this paper has good performance and generalization ability. Even if the target is small and varied in shape, it can still achieve highly accurate recognition.

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Section: Multi-channel Self-attention Feature Fusion Of Fine-grained ...mentioning

confidence: 99%

Enhancing Fine-Grained Image Recognition with Multi-Channel Self-Attention Mechanisms: A Focus on Fruit Fly Species Classification

Lu,

Yi,

2024

Applied Sciences

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Adaptive Neural Network-Based Finite-Time Tracking Control for Nonstrict Nonaffined MIMO Nonlinear Systems

Shu

Ding

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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Slip Estimation Model for Planetary Rover Using Gaussian Process Regression

et al. 2022

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Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models for China’s Mars rover, Zhurong, using Gaussian process regression (GPR). The model was able to predict not only the average value of the longitudinal (slip_x) and lateral slip (slip_y), but also the maximum possible value that slip_x and slip_y could reach. The training data were collected on two simulated soils, TYII-2 and JLU Mars-2, and the GA-BP algorithm was applied as a comparison. The analysis results demonstrated that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. The properties of the Martian soil near the Zhurong landing site were closer to the JLU Mars-2 simulated soil. The proposed GPR model had high estimation accuracy and estimation potential in slip value, and a 95% confidence interval that the rover could reach during motion. This work was part of a research effort aimed at ensuring the safety of Zhurong. The slip value may be used in subsequent path tracking research, and the slip confidence interval will be able to help guide path planning.

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Research on feature extraction and segmentation of rover wheel imprint

Cited by 3 publications

References 14 publications

Enhancing Fine-Grained Image Recognition with Multi-Channel Self-Attention Mechanisms: A Focus on Fruit Fly Species Classification

Enhancing Fine-Grained Image Recognition with Multi-Channel Self-Attention Mechanisms: A Focus on Fruit Fly Species Classification

Adaptive Neural Network-Based Finite-Time Tracking Control for Nonstrict Nonaffined MIMO Nonlinear Systems

Slip Estimation Model for Planetary Rover Using Gaussian Process Regression

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