ultralytics/yolov5: v3.0

Jocher, Glenn; Stoken, Alex; Borovec, Jirka; NanoCode,; ChristopherSTAN,; Liu, Changyu; Laughing,; Hogan, Adam; lorenzomammana,; tkianai,; yxNONG,; AlexWang,; Diaconu, Laurentiu; Marć, Paweł; wanghaoyang,; ah, ml; Doug,; Hatovix,; Poznanski, Jake; 于力军, Lijun Yu; changyu,; Rai, Prashant; Ferriday, Russ; Sullivan, Trevor; Wang, Xinyu; YuriRibeiro,; Claramunt, Eduard Reñé; hopesala,; dave, pritul; yzchen,

doi:10.5281/zenodo.3983579

Cited by 37 publications

(31 citation statements)

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“…In this section, we have firstly discussed the proposed DNN architecture deduced from the recent YOLOv5 network [18], followed by the auto-anchor generating method.…”

Section: Methodsmentioning

confidence: 99%

“…A PyTorch-based framework is proposed to attain short detection time that allows automated vehicles to make decisions timely. The net architecture is inspired by YOLOv5 [18], based on a single regression net for visual object detection. The optimized model is fine-tuned by using optimizations and loss functions to achieve the desired accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Flexible neural network for fast and accurate road scene perception

Mehtab

Yan

2022

Multimed Tools Appl

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Accurate object detection on the road is the most important requirement of autonomous vehicles. Extensive work has been accomplished for car, pedestrian, and cyclist detection; however, comparatively, very few efforts have been put into 2D object detection. In this article, a dynamic approach is investigated to design a perfect unified neural network that could achieve the best results based on our available hardware. The proposed architecture is based on CSPNet for feature extraction in an end-to-end way. The net extracts visual features by using backbone subnet, visual object detection is based on a feature pyramid network (FPN). In order to increase the net flexibility, an auto-anchor generating method is applied to the detection layer that makes the net suitable for any datasets. For fine-tuning the net, activation, optimization, and loss functions are considered along with multiple check points. The proposed net is trained and tested based on the benchmark KITTI dataset. Our extensive experiments show that the proposed model for visual object detection is superior to others, where other nets output very low accuracy for pedestrian and cyclist detection, our proposed model achieves 99.3% recall rate based on our dataset.

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“…In this section, we have firstly discussed the proposed DNN architecture deduced from the recent YOLOv5 network [18], followed by the auto-anchor generating method.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flexible neural network for fast and accurate road scene perception

Mehtab

Yan

2022

Multimed Tools Appl

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“…For the sliding window detector, the window can be regarded as the initial guess, and the boundary and category can be predicted at the same time. In recent years, yolov5 14 and SSD 15 are also one-stage detectors. Based on the peculiarity of the oral cavity dataset, we choose yolov5 as our object detection model.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, our object detection model is improved on the basis of yolov5 14 . The main purpose of molar detection is to identify the first molar and the second premolar.…”

Section: Molar Detectionmentioning

confidence: 99%

Detection of Children’s Molars Based on Noise Filter

Yan

Chen

Song

et al. 2022

Preprint

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Pit and fissure sealant is for children aged seven to twelve years to prevent molars from becoming caries. In this paper, we propose a new detection framework to identify whether children need pit and fissure sealing. We divide the framework into two parts: molar detection and molar classification. According to the characteristics of teeth, we propose to use the clustering method to filter the bounding box in the object detection part. In the region divided by clustering, we only keep one detection frame in the same category. In the classification part, we propose a noise filtering layer based on wavelet transform for feature extraction. We map the training samples to another space in the training process based on metric learning to increase the distance between categories and improve the accuracy of classification.

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“…When compared to Faster R-CNN, Yolo provides faster detection with the accuracy trade-off. This has been the core reason for its popularity and multiple extensions and adaptations like Yolov3 [13] and Yolov5 [15] have emerged from it.…”

Section: Yolomentioning

confidence: 99%

CNN Model & Tuning for Global Road Damage Detection

Vishwakarma

Vennelakanti

2020

2020 IEEE International Conference on Big Data (Big Data)

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This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of IEEE International Conference on Big Data 2020. We assess single and multi-stage network architectures for object detection and provide a benchmark using popular state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5. Data preparation for provided Road Damage training dataset, captured using smartphone camera from Czech, India and Japan is discussed. We studied the effect of training on a per country basis with respect to a single generalizable model. We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone. Additionally, we compare this to a one-stage Yolov5 model with Cross Stage Partial Network (CSPNet) backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones respectively. This shows the generalizability of the Resnet-50 model when compared to its more complex counterparts. Experiments were conducted using Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU. A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg F1 score to is made available at https://github.com/vishwakarmarhl/rdd2020

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ultralytics/yolov5: v3.0

Cited by 37 publications

References 0 publications

Flexible neural network for fast and accurate road scene perception

Flexible neural network for fast and accurate road scene perception

Detection of Children’s Molars Based on Noise Filter

CNN Model & Tuning for Global Road Damage Detection

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