ultralytics/yolov5: v2.0

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

doi:10.5281/zenodo.3958273

Cited by 4 publications

(2 citation statements)

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“…YOLO v4 [27] and v5 [39] adopt similar structures with YOLO v3 and modify the state-of-the-art methods such as cross-iteration batch normalization (CBN) [40], path aggregation network (PAN) [41], spatial attention module (SAM) [42] and so on, which make the network become efficient and suitable for single GPU training. Compared to YOLO v4, YOLO v5 is more flexible and smaller, so it is chosen to detect the safety helmet wearing to integrate with the hardware in the future.…”

Section: A the Structures Of Yolo Detectorsmentioning

confidence: 99%

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Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Han

Zeng

2022

IEEE Access

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Wearing safety helmets can effectively protect workers safety on construction sites. However, workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector with high speed and accuracy is urgently needed. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize. Therefore, this paper proposes a deep learning-based method to detect safety helmet wearing at a satisfactory accuracy with high detection speed. Our method chooses YOLO v5 as the baseline, then the fourth detection scale is added to predict more bounding boxes for small objects and the attention mechanism is adopted in the backbone of the network to construct more informative features for following concatenation operations. In order to overcome the defects caused by insufficient data, targeted data augmentation and transfer learning are used. Improvements caused by every modification are discussed in this paper. Finally, our model achieves 92.2% mean average precision, up 6.3% compared to the original algorithm, and it only takes 3.0 ms to detect an image at 640 × 640. These results demonstrate the robustness and feasibility of our model. Meanwhile, the size of our trained model is only 16.3 m, which means the model is easy to be deployed. At last, after obtaining a satisfactory model, a graphical user interface (GUI) is designed to make our algorithm more user-friendly.INDEX TERMS Safety helmet wearing detection, YOLO v5, four detection scales, attention mechanism, GUI design.

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Section: A the Structures Of Yolo Detectorsmentioning

confidence: 99%

“…They can help single-stage detectors achieve state-of-theart results [27]. Considering the trade-off between the speed and accuracy, this paper proposes a method to detect safety helmet wearing condition based on YOLO v5 [39], which can compute fast enough with a satisfactory accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Han

Zeng

2022

IEEE Access

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SL3D - Single Look 3D Object Detection based on RGB-D Images

Erabati

Araújo

2020

2020 Digital Image Computing: Techniques and Applications (DICTA)

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ReYOLO: A traffic sign detector based on network reparameterization and features adaptive weighting

Zhang

Zheng

Xie

et al. 2022

AIS

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Traffic sign detection is a challenging task. Although existing deep learning techniques have made great progress in detecting traffic signs, there are still many unsolved challenges. We propose a novel traffic sign detection network named ReYOLO that learns rich contextual information and senses scale variations to efficiently detect small and ambiguous traffic signs in the wild. Specifically, we first replace the conventional convolutional block with modules that are built by structural reparameterization methods and are embedded into bigger structures, thus decoupling the training structures and the inference structures using parameter transformation, and allowing the model to learn more effective features. We then design a novel weighting mechanism which can be embedded into a feature pyramid to exploit foreground features at different scales to narrow the semantic gap between multiple scales. To fully evaluate the proposed method, we conduct experiments on a traditional traffic sign dataset GTSDB as well as two new traffic sign datasets TT100K and CCTSDB2021, achieving 97.2%, 68.3% and 83.9% mAP (Mean Average Precision) for the three-class detection challenge in these three datasets.

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ultralytics/yolov5: v2.0

Cited by 4 publications

References 0 publications

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

SL3D - Single Look 3D Object Detection based on RGB-D Images

ReYOLO: A traffic sign detector based on network reparameterization and features adaptive weighting

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