Real-Time Cattle Pose Estimation Based on Improved RTMPose

Li, Xiaowu; Sun, Kun; Fan, Hongbo; He, Zihan

doi:10.3390/agriculture13101938

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…RTMpose [29]: RTMpose is a novel pose estimation model that integrates a recurrent temporal module (RTM) into the pose estimation framework, enabling the model to capture temporal dependencies and improve the accuracy of pose estimation over time.…”

Section: Baselinementioning

confidence: 99%

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Wang,

2024

Electronics

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In the field of multimodal robotics, achieving comprehensive and accurate perception of the surrounding environment is a highly sought-after objective. However, current methods still have limitations in motion keypoint detection, especially in scenarios involving small target detection and complex scenes. To address these challenges, we propose an innovative approach known as YOLOv8-PoseBoost. This method introduces the Channel Attention Module (CBAM) to enhance the network’s focus on small targets, thereby increasing sensitivity to small target individuals. Additionally, we employ multiple scale detection heads, enabling the algorithm to comprehensively detect individuals of varying sizes in images. The incorporation of cross-level connectivity channels further enhances the fusion of features between shallow and deep networks, reducing the rate of missed detections for small target individuals. We also introduce a Scale Invariant Intersection over Union (SIoU) redefined bounding box regression localization loss function, which accelerates model training convergence and improves detection accuracy. Through a series of experiments, we validate YOLOv8-PoseBoost’s outstanding performance in motion keypoint detection for small targets and complex scenes. This innovative approach provides an effective solution for enhancing the perception and execution capabilities of multimodal robots. It has the potential to drive the development of multimodal robots across various application domains, holding both theoretical and practical significance.

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Section: Baselinementioning

confidence: 99%

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Wang,

2024

Electronics

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Research on vehicle A-pillar visualization based on A-pillar detection and decision tree model

Yuan,

Sun,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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When turning or passing through intersections, the vehicle’s A-pillar blind spot may obscure road conditions. Using cameras to capture and display external images on the A-pillar screen can eliminate the A-pillar blind spot. However, this solution still faces a mismatch between the displayed screen image and the surrounding environment image. To obtain more accurate blind spot images, accurate blind spot cropping areas are calculated through vehicle A-pillar detection and image registration technology. By introducing eye position and posture as features and considering the corresponding blind spot cropping areas as target variables, a decision tree model is trained to predict the blind spot cropping areas. This model can rapidly and accurately predict the blind spot areas for display on the A-pillar screen, with an average pixel error of 12.7, a coefficient of determination of 0.85, and an image processing speed within one millisecond. Experiments show that the A-pillar blind spot visualization system based on A-pillar detection and decision tree model prediction effectively eliminates the A-pillar blind spot, enhancing driving safety.

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High-throughput plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

Xu,

Shen,

Wei

et al. 2024

Preprint

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Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

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Real-Time Cattle Pose Estimation Based on Improved RTMPose

Cited by 3 publications

References 37 publications

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Research on vehicle A-pillar visualization based on A-pillar detection and decision tree model

High-throughput plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

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