Correction to: YOLOP: You Only Look Once for Panoptic Driving Perception

Wu, Dong; Liao, Man-Wen; Zhang, Weitian; Wang, Xinggang; Bai, Xiang; Cheng, Wenqing; Yu, Wen

doi:10.1007/s11633-023-1452-6

Cited by 3 publications

(1 citation statement)

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“…With the rapid development of deep learning technology, the capability of target detection models in image recognition and classification has significantly improved ( Mu and Zeng, 2019 ). The target detection models are divided into single-stage models (e.g., Single Shot MultiBox Detector, You Only Look Once) and two-stage models (e.g., R-CNN, Faster R-CNN)( Ning et al., 2017 ; Bharati and Pramanik, 2020 ; Wu et al., 2022 ; Xu et al., 2023 ). Both the single-stage and two-stage models have high accuracy on wheat ears detection tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

Shen,

Zhang,

Liu

et al. 2023

Front. Plant Sci.

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IntroductionRecognizing wheat ears plays a crucial role in predicting wheat yield. Employing deep learning methods for wheat ears identification is the mainstream method in current research and applications. However, such methods still face challenges, such as high computational parameter volume, large model weights, and slow processing speeds, making it difficult to apply them for real-time identification tasks on limited hardware resources in the wheat field. Therefore, exploring lightweight wheat ears detection methods for real-time recognition holds significant importance.MethodsThis study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations.Results and discussionThis study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs and mAP of this model are 2.9 MB, 2.5 * 109 and 94.8%, respectively. The linear fitting determination coefficients R2 for the model test result and actual value of global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ears counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources.

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Section: Literature Reviewmentioning

confidence: 99%

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

Shen,

Zhang,

Liu

et al. 2023

Front. Plant Sci.

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Local many-to-many matching via ROI feature decomposition for multi-object tracking

Yang,

Ban,

Liu

2024

SIViP

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A Deep-Learning-Based CPR Action Standardization Method

Li,

Yin,

et al. 2024

Sensors

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In emergency situations, ensuring standardized cardiopulmonary resuscitation (CPR) actions is crucial. However, current automated external defibrillators (AEDs) lack methods to determine whether CPR actions are performed correctly, leading to inconsistent CPR quality. To address this issue, we introduce a novel method called deep-learning-based CPR action standardization (DLCAS). This method involves three parts. First, it detects correct posture using OpenPose to recognize skeletal points. Second, it identifies a marker wristband with our CPR-Detection algorithm and measures compression depth, count, and frequency using a depth algorithm. Finally, we optimize the algorithm for edge devices to enhance real-time processing speed. Extensive experiments on our custom dataset have shown that the CPR-Detection algorithm achieves a mAP0.5 of 97.04%, while reducing parameters to 0.20 M and FLOPs to 132.15 K. In a complete CPR operation procedure, the depth measurement solution achieves an accuracy of 90% with a margin of error less than 1 cm, while the count and frequency measurements achieve 98% accuracy with a margin of error less than two counts. Our method meets the real-time requirements in medical scenarios, and the processing speed on edge devices has increased from 8 fps to 25 fps.

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Correction to: YOLOP: You Only Look Once for Panoptic Driving Perception

Cited by 3 publications

References 0 publications

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

Local many-to-many matching via ROI feature decomposition for multi-object tracking

A Deep-Learning-Based CPR Action Standardization Method

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