2022电子物联网毕设一套基于深度学习的扶梯行人异常行为检测研究（论文+程序源代码+数据）定稿.zip原创优质

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2025-09-05
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2022电子物联网毕设一套基于深度学习的扶梯行人异常行为检测研究（论文+程序源代码+数据）定稿.zip

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1814110811_王彬_基于深度学习的扶梯行人异常行为检测研究.pdf(1.21 MB)
1814110811_王彬_代码
- .idea
  - .gitignore(50 B)
  - inspectionProfiles
    - profiles_settings.xml(174 B)
  - misc.xml(305 B)
  - modules.xml(301 B)
  - workspace.xml(4.36 KB)
  - yolov5-mask-42-master.iml(492 B)
- Arial.ttf(755.11 KB)
- LICENSE(34.3 KB)
- __pycache__
  - val.cpython-38.pyc(12.14 KB)
- accident.wav(62.23 KB)
- dange.wav(55.98 KB)
- data
  - Argoverse.yaml(2.69 KB)
  - GlobalWheat2020.yaml(1.84 KB)
  - Objects365.yaml(7.91 KB)
  - SKU-110K.yaml(2.3 KB)
  - VOC.yaml(3.3 KB)
  - VisDrone.yaml(2.86 KB)
  - coco.yaml(2.3 KB)
  - coco128.yaml(1.67 KB)
  - data.yaml(306 B)
  - hyps
    - hyp.finetune.yaml(907 B)
    - hyp.finetune_objects365.yaml(460 B)
    - hyp.scratch-high.yaml(1.64 KB)
    - hyp.scratch-low.yaml(1.65 KB)
    - hyp.scratch-med.yaml(1.65 KB)
    - hyp.scratch.yaml(1.62 KB)
  - images
    - phone
      - phone_1310.jpg(392.44 KB)
      - phone_2096.jpg(222.89 KB)
      - phone_2402.jpg(181.3 KB)
      - phone_2423.jpg(196.67 KB)
      - phone_419.jpg(646.13 KB)
      - phone_633.jpg(343.56 KB)
      - phone_689.jpg(314.93 KB)
      - phone_89.jpg(444.2 KB)
  - mask_data.yaml(323 B)
  - scripts
    - download_weights.sh(523 B)
    - get_coco.sh(900 B)
    - get_coco128.sh(615 B)
  - xView.yaml(4.92 KB)
- data_gen.py(7.91 KB)
- demo
  - images
    - test
    - train
    - val
  - labels
    - test
    - train
      - classes.txt(18 B)
      - sj1.txt(38 B)
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      - sj96.txt(38 B)
      - sj97.txt(38 B)
      - sj98.txt(38 B)
      - sj99.txt(38 B)
    - val
      - classes.txt(18 B)
      - sj1.txt(38 B)
      - sj10.txt(38 B)
      - sj100.txt(38 B)
      - sj101.txt(38 B)
      - sj102.txt(38 B)
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      - sj14.txt(38 B)
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      - sj144.txt(38 B)
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      - sj15.txt(38 B)
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      - sj16.txt(38 B)
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      - sj43.txt(38 B)
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      - sj5.txt(38 B)
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      - sj6.txt(38 B)
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      - sj95.txt(38 B)
      - sj96.txt(38 B)
      - sj97.txt(38 B)
      - sj98.txt(38 B)
      - sj99.txt(38 B)
- denger.wav(67.54 KB)
- detect.py(12.23 KB)
- docs
  - Dockerfile(2.06 KB)
  - README.md(14.4 KB)
  - tutorial.ipynb(54.54 KB)
- export.py(16.46 KB)
- hubconf.py(6.06 KB)
- images
  - UI
    - R-C.jfif(106.1 KB)
    - lufei.png(215.7 KB)
    - view.jfif(102.21 KB)
    - viewp.png(371.64 KB)
    - wip.png(151.72 KB)
  - tmp
    - single_result.jpg(127.82 KB)
    - single_result_vid.jpg(134.66 KB)
    - tmp_upload.jpeg(21.27 KB)
    - tmp_upload.jpg(127.06 KB)
    - tmp_upload.png(1.63 MB)
    - upload_show_result.jpg(123.29 KB)
- models
  - __init__.py(0 B)
  - __pycache__
    - __init__.cpython-38.pyc(134 B)
    - common.cpython-38.pyc(25.93 KB)
    - experimental.cpython-38.pyc(4.76 KB)
    - yolo.cpython-38.pyc(12.62 KB)
  - common.py(26.71 KB)
  - experimental.py(4.48 KB)
  - hub
    - anchors.yaml(3.26 KB)
    - yolov3-spp.yaml(1.53 KB)
    - yolov3-tiny.yaml(1.2 KB)
    - yolov3.yaml(1.52 KB)
    - yolov5-bifpn.yaml(1.39 KB)
    - yolov5-fpn.yaml(1.19 KB)
    - yolov5-p2.yaml(1.62 KB)
    - yolov5-p6.yaml(1.66 KB)
    - yolov5-p7.yaml(2.03 KB)
    - yolov5-panet.yaml(1.37 KB)
    - yolov5l6.yaml(1.78 KB)
    - yolov5m6.yaml(1.78 KB)
    - yolov5n6.yaml(1.78 KB)
    - yolov5s-ghost.yaml(1.45 KB)
    - yolov5s-transformer.yaml(1.41 KB)
    - yolov5s6.yaml(1.78 KB)
    - yolov5x6.yaml(1.78 KB)
  - mask_yolov5l.yaml(1.37 KB)
  - mask_yolov5m.yaml(1.37 KB)
  - mask_yolov5s.yaml(1.37 KB)
  - tf.py(20.18 KB)
  - yolo.py(14.89 KB)
  - yolov5l.yaml(1.37 KB)
  - yolov5m.yaml(1.37 KB)
  - yolov5n.yaml(1.37 KB)
  - yolov5s.yaml(1.37 KB)
  - yolov5x.yaml(1.37 KB)
- pip(0 B)
- pretrained
  - yolov5s.pt(14.02 MB)
- runs
  - detect
    - exp
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  - train
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      - train_batch1.jpg(189.38 KB)
      - train_batch2.jpg(106.78 KB)
      - weights
    - exp2
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      - hyp.yaml(400 B)
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      - results.csv(9.22 KB)
      - train_batch0.jpg(77.17 KB)
      - train_batch1.jpg(155.4 KB)
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      - weights
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        last.pt(54.14 MB)
    - exp3
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      - events.out.tfevents.1651144690.LAPTOP-K6H7FAKU.15140.0(965.44 KB)
      - hyp.yaml(400 B)
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      - results.csv(29.1 KB)
      - results.png(239.4 KB)
      - train_batch0.jpg(37.08 KB)
      - train_batch1.jpg(144.69 KB)
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      - weights
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- train.py(32.48 KB)
- utils
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    - datasets.cpython-38.pyc(34.8 KB)
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    - metrics.cpython-38.pyc(10.71 KB)
    - plots.cpython-38.pyc(17.57 KB)
    - torch_utils.cpython-38.pyc(11.63 KB)
  - activations.py(3.69 KB)
  - augmentations.py(11.46 KB)
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    - userdata.sh(1.22 KB)
  - callbacks.py(2.2 KB)
  - datasets.py(44.07 KB)
  - datasets_not_print.py(44.02 KB)
  - downloads.py(5.99 KB)
  - flask_rest_api
    - README.md(1.67 KB)
    - example_request.py(299 B)
    - restapi.py(1.05 KB)
  - general.py(34.01 KB)
  - google_app_engine
    - Dockerfile(821 B)
    - additional_requirements.txt(105 B)
    - app.yaml(174 B)
  - loggers
    - __init__.py(6.72 KB)
    - __pycache__
      - __init__.cpython-38.pyc(6.48 KB)
    - wandb
      - README.md(10.18 KB)
      - __init__.py(0 B)
      - __pycache__
        __init__.cpython-38.pyc(147 B)
        wandb_utils.cpython-38.pyc(18.75 KB)
      - log_dataset.py(1.01 KB)
      - sweep.py(1.12 KB)
      - sweep.yaml(2.41 KB)
      - wandb_utils.py(25.35 KB)
  - loss.py(9.43 KB)
  - metrics.py(13.34 KB)
  - plots.py(20.02 KB)
  - torch_utils.py(13.14 KB)
  - ui
    - __init__.py(175 B)
    - ji.py(6.23 KB)
    - server_main.py(468 B)
    - train_server.py(32.27 KB)
    - x.py(3.02 KB)
- val.py(17.4 KB)
- window.py(26.77 KB)
- window1(1).py(31.28 KB)
- window13.py(28.73 KB)
- yic.wav(100.98 KB)

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2022电子物联网毕设一套 基于深度学习的扶梯行人异常行为检测研究（论文+程序源代码+数据）定稿.zip

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