Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Jia, Wei; Xu, Shiquan; Liang, Zhen; Min, Hai; Li, Shujie

doi:10.1049/ipr2.12295

Cited by 112 publications

(50 citation statements)

References 65 publications

(82 reference statements)

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“…Once again, mask detectors [16] have been proposed that leverage anchors generated and data augmentation to fit a model to the use case better. More complex systems for helmet detection [10] also do a great job at leveraging the contextual information around small objects to isolate them and facilitate their detection. However, their approach is not quite universally applicable and comes at the cost of introducing a two-step process.…”

Section: Systems Using and Modifying Yolov5mentioning

confidence: 99%

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

Benjumea¹,

Teeti²,

Cuzzolin³

et al. 2021

Preprint

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As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors. While our naked eyes are able to extract contextual information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects (that is, objects that occupy a small pixel area in the input image) a genuinely challenging task for machines and a wide-open research field. This study explores how the popular YOLOv5 object detector can be modified to improve its performance in detecting smaller objects, with a particular application in autonomous racing. To achieve this, we investigate how replacing certain structural elements of the model (as well as their connections and other parameters) can affect performance and inference time. In doing so, we propose a series of models at different scales, which we name 'YOLO-Z', and which display an improvement of up to 6.9% in mAP when detecting smaller objects at 50% IOU, at the cost of just a 3ms increase in inference time compared to the original YOLOv5. Our objective is to inform future research on the potential of adjusting a popular detector such as YOLOv5 to address specific tasks and provide insights on how specific changes can impact small object detection. Such findings, applied to the broader context of autonomous vehicles, could increase the amount of contextual information available to such systems.

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Section: Systems Using and Modifying Yolov5mentioning

confidence: 99%

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

Benjumea¹,

Teeti²,

Cuzzolin³

et al. 2021

Preprint

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show abstract

“…First, we use the LabelImg software to set the label for the five lettuce datasets of SP as Plant. Then, YOLO-v5 ( Jia et al, 2021 ; Kasper-eulaers et al, 2021 ; Liu W. et al, 2021 ), the most advanced algorithm of Yolo series, was used to detect the lettuce plant in SP , namely separating from seedling cotton. After training an object detection model independently in SP , we cut the lettuce plants according to the coordinates of the location of each plant predicted by the target detection model.…”

Section: Methodsmentioning

confidence: 99%

YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings

Zhang

2022

Front. Plant Sci.

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Accurate identification of crop varieties is an important aspect of smart agriculture, which is not only essential for the management of later crop differences, but also has a significant effect on unmanned operations in planting scenarios such as facility greenhouses. In this study, five kinds of lettuce under the cultivation conditions of greenhouses were used as the research object, and a classification model of lettuce varieties with multiple growth stages was established. First of all, we used the-state-of-the-art method VOLO-D1 to establish a variety classification model for the 7 growth stages of the entire growth process. The results found that the performance of the lettuce variety classification model in the SP stage needs to be improved, but the classification effect of the model at other stages is close to 100%; Secondly, based on the challenges of the SP stage dataset, we combined the advantages of the target detection mechanism and the target classification mechanism, innovatively proposed a new method of variety identification for the SP stage, called YOLO-VOLO-LS. Finally, we used this method to model and analyze the classification of lettuce varieties in the SP stage. The result shows that the method can achieve excellent results of 95.961, 93.452, 96.059, 96.014, 96.039 in Val-acc, Test-acc, Recall, Precision, F1-score, respectively. Therefore, the method proposed in this study has a certain reference value for the accurate identification of varieties in the early growth stage of crops.

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“…Because Fast R-CNN was the combining hand-crafted and deep convolutional features method is used, there are limitations in detecting objects or humans [27]. The basic structure of the previous YOLOv5 [28] is largely divided into the backbone network part, the neck part, and the head part, as shown in Figure 1 [29].…”

Section: Yolov5_ours Networkmentioning

confidence: 99%

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

2022

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With the recent development of drone technology, object detection technology is emerging, and these technologies can also be applied to illegal immigrants, industrial and natural disasters, and missing people and objects. In this paper, we would like to explore ways to increase object detection performance in these situations. Photography was conducted in an environment where it was confusing to detect an object. The experimental data were based on photographs that created various environmental conditions, such as changes in the altitude of the drone, when there was no light, and taking pictures in various conditions. All the data used in the experiment were taken with F11 4K PRO drone and VisDrone dataset. In this study, we propose an improved performance of the original YOLOv5 model. We applied the obtained data to each model: the original YOLOv5 model and the improved YOLOv5_Ours model, to calculate the key indicators. The main indicators are precision, recall, F-1 score, and mAP (0.5), and the YOLOv5_Ours values of mAP (0.5) and function loss were improved by comparing it with the original YOLOv5 model. Finally, the conclusion was drawn based on the data comparing the original YOLOv5 model and the improved YOLOv5_Ours model. As a result of the analysis, we were able to arrive at a conclusion on the best model of object detection under various conditions.

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Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Cited by 112 publications

References 65 publications

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

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