Deep driver behavior detection model based on human brain consolidated learning for shared autonomy systems

Huang, Tao; Fu, Rui; Chen, Yunxing

doi:10.1016/j.measurement.2021.109463

Cited by 19 publications

(3 citation statements)

References 29 publications

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“…In order to evaluate the effectiveness of the network model in detecting the germination of wild rice seeds, the trained model was evaluated using the precision rate P (precision), the recall rate R (recall), the F1-score, and the mAP@0.5 ( Abouelnaga et al., 2018 ; Kong et al., 2019 ; Huang et al., 2021 ; Mariusz and Marek, 2022 ) (mean average precision) as assessment metrics so as to validate and compare the performance of the model. Among them, precision rate P denotes the accuracy of the model in predicting the target, recall rate R denotes the success of the model in searching the target, and F1-score is the reconciled average of precision and recall, which is considered to be equally important, with the maximum of 1 and the minimum of 0. mAP@0.5 measures how good the model is in detecting all the categories.…”

Section: Resultsmentioning

confidence: 99%

SGR-YOLO: a method for detecting seed germination rate in wild rice

Yao,

Zheng,

Zhou

et al. 2024

Front. Plant Sci.

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Seed germination rate is one of the important indicators in measuring seed quality and seed germination ability, and it is also an important basis for evaluating the growth potential and planting effect of seeds. In order to detect seed germination rates more efficiently and achieve automated detection, this study focuses on wild rice as the research subject. A novel method for detecting wild rice germination rates is introduced, leveraging the SGR-YOLO model through deep learning techniques. The SGR-YOLO model incorporates the convolutional block attention module (efficient channel attention (ECA)) in the Backbone, adopts the structure of bi-directional feature pyramid network (BiFPN) in the Neck part, adopts the generalized intersection over union (GIOU) function as the loss function in the Prediction part, and adopts the GIOU function as the loss function by setting the weighting coefficient to accelerate the detection of the seed germination rate. In the Prediction part, the GIOU function is used as the loss function to accelerate the learning of high-confidence targets by setting the weight coefficients to further improve the detection accuracy of seed germination rate. The results showed that the accuracy of the SGR-YOLO model for wild rice seed germination discrimination was 94% for the hydroponic box and 98.2% for the Petri dish. The errors of germination potential, germination index, and average germination days detected by SGR-YOLO using the manual statistics were 0.4%, 2.2, and 0.9 days, respectively, in the hydroponic box and 0.5%, 0.5, and 0.24 days, respectively, in the Petri dish. The above results showed that the SGR-YOLO model can realize the rapid detection of germination rate, germination potential, germination index, and average germination days of wild rice seeds, which can provide a reference for the rapid detection of crop seed germination rate.

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Section: Resultsmentioning

confidence: 99%

SGR-YOLO: a method for detecting seed germination rate in wild rice

Yao,

Zheng,

Zhou

et al. 2024

Front. Plant Sci.

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“…Vehicle detection is an important part of autonomous vehicles and road traffic, it can count vehicle information, traffic flow information, etc., which provides important information support for the making decision of government [1,2]. Traditionally, traffic video information is processed manually, but the development of artificial intelligence techniques has brought transformative progress to society, especially the continuous development in the field of computer vision, which makes the camera human-like processing ability [3][4][5]. Therefore, using computer vision-related methods for vehicle monitoring can greatly improve the efficiency of information processing.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection Using the Light Weight Model Based on YOLOv5

Lyu

2023

J. Phys.: Conf. Ser.

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Vehicle detection is important for the administration because it’s an important part of the intelligent traffic management system. It can help to make the best decision for the government and provide the data for intelligent roadside equipment. And can reduce road congestion and emissions, reduce the incidence of traffic accidents, improve road safety and reduce damage caused by accidents. The current method of object detection needs large computing resources and makes the roadside equipment costly or unable to meet the computing resources. So, this paper proposed a light weight detection model based on YOLOv5. This model reduces the parameter and FLOPs by using the deepthwise separable convolution CBAM to improve the performance of vehicle detection. And this paper reduces the input image size and halves the feature channel to reduce the amount of computation. The experiment result shows that the proposed model is 8 times smaller than YOLOv5-s while the mAP only decreases 6% on the UA-DETRAC dataset, significantly outperforming the other methods. This paper provided an idea for implementing a light weight model and a way to reduce the application cost of roadside smart devices.

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“…Influenced by adverse weather conditions and uncertain traffic flow, the accuracy and efficiency of visual perception drop off sharply (Huang et al, 2021). And the perception failure causality is still unclear.…”

Section: Introductionmentioning

confidence: 99%

Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation

Zhao¹,

Yun²,

Chai³

et al. 2022

Preprint

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The complex driving environment brings great challenges to the visual perception of autonomous vehicles.It's essential to extract clear and explainable information from the complex road and traffic scenarios and offer clues to decision and control. However, the previous scene explanation had been implemented as a separate model. The black box model makes it difficult to interpret the driving environment. It cannot detect comprehensive textual information and requires a high computational load and time consumption.Thus, this study proposed a comprehensive and efficient textual explanation model. From 336k video frames of the driving environment, critical images of complex road and traffic scenarios were selected into a dataset.Through transfer learning, this study established an accurate and efficient segmentation model to obtain the critical traffic elements in the environment. Based on the XGBoost algorithm, a comprehensive model was developed. The model provided textual information about states of traffic elements, the motion of conflict objects, and scenario complexity. The approach was verified on the real-world road. It improved the perception accuracy of critical traffic elements to 78.8%. The time consumption reached 13 minutes for each epoch, which was 11.5 times more efficient than the pre-trained network. The textual information analyzed from the model was also accordant with reality. The findings offer clear and explainable information about the complex driving environment, which lays a foundation for subsequent decision and control. It can improve the visual perception ability and enrich the prior knowledge and judgments of complex traffic situations.

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Deep driver behavior detection model based on human brain consolidated learning for shared autonomy systems

Cited by 19 publications

References 29 publications

SGR-YOLO: a method for detecting seed germination rate in wild rice

SGR-YOLO: a method for detecting seed germination rate in wild rice

Vehicle Detection Using the Light Weight Model Based on YOLOv5

Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation

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