{"id":17587,"date":"2025-04-19T00:07:12","date_gmt":"2025-04-18T16:07:12","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/17587.html"},"modified":"2025-04-19T00:07:12","modified_gmt":"2025-04-18T16:07:12","slug":"yolo%e7%9b%ae%e6%a0%87%e6%a3%80%e6%b5%8b%e4%bb%a3%e7%a0%81%e7%a7%bb%e6%a4%8d%e5%88%b0%e6%9c%8d%e5%8a%a1%e5%99%a8%e3%80%81jetson-nano%e6%88%96%e6%a0%91%e8%8e%93%e6%b4%be%e4%b8%8a-%e5%b9%b6%e8%bf%9b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/17587.html","title":{"rendered":"YOLO\u76ee\u6807\u68c0\u6d4b\u4ee3\u7801\u79fb\u690d\u5230\u670d\u52a1\u5668\u3001Jetson Nano\u6216\u6811\u8393\u6d3e\u4e0a \u5e76\u8fdb\u884c\u5b9e\u65f6\u68c0\u6d4b \u901f\u5ea6\u7b49\u6027\u80fd\u7684\u4f18\u5316"},"content":{"rendered":"

\u5c06YOLO\u76ee\u6807\u68c0\u6d4b\u4ee3\u7801\u79fb\u690d\u5230Jetson Nano\u6216\u6811\u8393\u6d3e\u4e0a\uff0c\u4e3b\u8981\u5206\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n

  • \u786c\u4ef6\u4e0e\u8f6f\u4ef6\u51c6\u5907\uff1a\u786e\u4fdd\u4f60\u6709\u5408\u9002\u7684\u786c\u4ef6\u8bbe\u5907\uff08\u5982Jetson Nano\u6216\u6811\u8393\u6d3e\uff09\uff0c\u5e76\u5b89\u88c5\u76f8\u5e94\u7684\u64cd\u4f5c\u7cfb\u7edf\uff08\u5982JetPack for Jetson Nano\u6216Raspberry Pi OS for \u6811\u8393\u6d3e\uff09\u3002<\/li>\n
  • \u73af\u5883\u642d\u5efa\uff1a\u5728\u8bbe\u5907\u4e0a\u5b89\u88c5Python\u3001pip\u7b49\u57fa\u7840\u5de5\u5177\uff0c\u521b\u5efa\u865a\u62df\u73af\u5883\uff08\u53ef\u9009\uff09\uff0c\u5e76\u5b89\u88c5YOLO\u6240\u9700\u7684\u4f9d\u8d56\u5e93\uff0c\u5982OpenCV\u3001NumPy\u3001PyTorch\u6216TensorFlow\u3002<\/li>\n
  • \u4e0b\u8f7dYOLO\u6a21\u578b\u548c\u6743\u91cd\uff1a\u4eceYOLO\u7684\u5b98\u65b9GitHub\u4ed3\u5e93\u514b\u9686\u4ee3\u7801\uff0c\u5e76\u4e0b\u8f7d\u9884\u8bad\u7ec3\u7684\u6743\u91cd\u6587\u4ef6\u3002<\/li>\n
  • \u8fd0\u884c\u68c0\u6d4b\u811a\u672c\uff1a\u4f7f\u7528YOLO\u63d0\u4f9b\u7684\u68c0\u6d4b\u811a\u672c\uff0c\u6307\u5b9a\u6743\u91cd\u6587\u4ef6\u3001\u56fe\u50cf\u5c3a\u5bf8\u3001\u7f6e\u4fe1\u5ea6\u9608\u503c\u548c\u6570\u636e\u6e90\uff08\u5982\u6444\u50cf\u5934\u6216\u56fe\u50cf\u6587\u4ef6\uff09\uff0c\u8fd0\u884c\u76ee\u6807\u68c0\u6d4b\u3002<\/li>\n
  • \u4f18\u5316\u6027\u80fd\uff1a\u6839\u636e\u8bbe\u5907\u7c7b\u578b\uff0c\u542f\u7528GPU\u52a0\u901f\uff08\u5982Jetson Nano\u7684CUDA\u6216\u6811\u8393\u6d3e\u7684OpenCV GPU\u52a0\u901f\uff09\uff0c\u5e76\u9009\u62e9\u5408\u9002\u7684\u8f7b\u91cf\u7ea7YOLO\u6a21\u578b\u4ee5\u63d0\u9ad8\u6027\u80fd\u3002<\/li>\n

    \u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u5728Jetson Nano\u6216\u6811\u8393\u6d3e\u4e0a\u6210\u529f\u90e8\u7f72YOLO\u76ee\u6807\u68c0\u6d4b\u7cfb\u7edf\uff0c\u5b9e\u73b0\u5b9e\u65f6\u7684\u76ee\u6807\u8bc6\u522b\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n

      \u8fb9\u7f18\u8ba1\u7b97\u53ef\u4ee5\u8ba9\u8fd9\u4e9b\u5c0f\u578b\u3001\u4f4e\u529f\u8017\u7684\u8bbe\u5907\u5177\u5907\u5f3a\u5927\u7684\u76ee\u6807\u68c0\u6d4b\u80fd\u529b\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u8fb9\u7f18\u8ba1\u7b97\u573a\u666f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u8be6\u7ec6\u7684\u6b65\u9aa4\u6307\u5357\uff0c\u5e2e\u52a9\u4f60\u5b8c\u6210\u79fb\u690d\u8fc7\u7a0b\u3002<\/p>\n

    \u4e00\u3001\u51c6\u5907\u5de5\u4f5c<\/h3>\n

    \u8f6f\u4ef6\u4ee3\u7801\u51c6\u5907 \u9996\u5148\u4ee5\u884c\u4eba\u76d1\u6d4b\u4e3a\u4f8b\u5b50\u8bf4\u660e<\/p>\n

    \u4e3a\u4e86\u5b9e\u73b0\u5b9e\u65f6\u83b7\u53d6\u89c6\u9891\u6d41\u5e76\u4f7f\u7528 YOLO \u76ee\u6807\u68c0\u6d4b\u7b97\u6cd5\u76d1\u6d4b\u884c\u4eba\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Python \u548c OpenCV \u5e93\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4ece\u6444\u50cf\u5934\u83b7\u53d6\u5b9e\u65f6\u89c6\u9891\u6d41\uff0c\u5e76\u4f7f\u7528 YOLOv5 \u6a21\u578b\u68c0\u6d4b\u884c\u4eba\u3002<\/p>\n

    \u73af\u5883\u51c6\u5907<\/h4>\n
  • \n

    \u5b89\u88c5\u4f9d\u8d56\u5e93<\/p>\n

    bash<\/p>\n

    pip install opencv-python numpy torch torchvision <\/li>\n

  • \n

    \u514b\u9686 YOLOv5 \u4ed3\u5e93\u5e76\u5b89\u88c5\u4f9d\u8d56<\/p>\n

    bash<\/p>\n

    git clone https:\/\/github.com\/ultralytics\/yolov5.git
    \ncd yolov5
    \npip install -r requirements.txt <\/li>\n

  • \n

    \u4e0b\u8f7d\u9884\u8bad\u7ec3\u6743\u91cd<\/p>\n

    bash<\/p>\n

    wget https:\/\/github.com\/ultralytics\/yolov5\/releases\/download\/v1.0\/yolov5s.pt <\/li>\n

    \u4ee3\u7801\u5b9e\u73b0<\/h4>\n

    python<\/p>\n

    import cv2
    \nimport torch
    \nfrom yolov5.models.experimental import attempt_load
    \nfrom yolov5.utils.datasets import letterbox
    \nfrom yolov5.utils.general import non_max_suppression, scale_coords
    \nfrom yolov5.utils.torch_utils import select_device<\/p>\n

    # \u52a0\u8f7d YOLOv5 \u6a21\u578b
    \nweights = \\’yolov5s.pt\\’ # \u6a21\u578b\u6743\u91cd\u6587\u4ef6
    \ndevice = select_device(\\’\\’) # \u9009\u62e9\u8bbe\u5907\uff0c\\’\\’ \u8868\u793a\u81ea\u52a8\u9009\u62e9
    \nmodel = attempt_load(weights, map_location=device) # \u52a0\u8f7d\u6a21\u578b
    \nstride = int(model.stride.max()) # \u6a21\u578b\u7684\u6b65\u957f<\/p>\n

    # \u8bbe\u7f6e\u6444\u50cf\u5934
    \ncap = cv2.VideoCapture(0) # 0 \u8868\u793a\u9ed8\u8ba4\u6444\u50cf\u5934<\/p>\n

    # \u5faa\u73af\u8bfb\u53d6\u89c6\u9891\u5e27
    \nwhile True:
    \n ret, frame = cap.read()
    \n if not ret:
    \n break<\/p>\n

    # \u56fe\u50cf\u9884\u5904\u7406
    \n img = letterbox(frame, new_shape=640, stride=stride)[0]
    \n img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
    \n img = np.ascontiguousarray(img)
    \n img = torch.from_numpy(img).to(device)
    \n img = img.float() # uint8 to fp16\/32
    \n img \/= 255.0 # \u56fe\u50cf\u5f52\u4e00\u5316
    \n if img.ndimension() == 3:
    \n img = img.unsqueeze(0)<\/p>\n

    # \u6a21\u578b\u63a8\u7406
    \n pred = model(img, augment=False, visualize=False)[0]<\/p>\n

    # \u5e94\u7528 NMS
    \n pred = non_max_suppression(pred, 0.25, 0.45, classes=None, agnostic=False)<\/p>\n

    # \u5904\u7406\u68c0\u6d4b\u7ed3\u679c
    \n for i, det in enumerate(pred): # \u904d\u5386\u6bcf\u4e2a\u68c0\u6d4b\u7ed3\u679c
    \n s = \\’\\’
    \n s += \\’%gx%g \\’ % img.shape[2:] # \u6253\u5370\u56fe\u50cf\u5c3a\u5bf8
    \n gn = torch.tensor(frame.shape)[[1, 0, 1, 0]] # \u5f52\u4e00\u5316\u589e\u76ca<\/p>\n

    if len(det):
    \n # \u5c06\u5750\u6807\u4ece [0, 1] \u8f6c\u6362\u4e3a\u50cf\u7d20\u5750\u6807
    \n det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()<\/p>\n

    # \u904d\u5386\u6bcf\u4e2a\u68c0\u6d4b\u5230\u7684\u76ee\u6807
    \n for *xyxy, conf, cls in reversed(det):
    \n if int(cls) == 0: # 0 \u8868\u793a\u884c\u4eba
    \n x1, y1, x2, y2 = map(int, xyxy) # \u5750\u6807\u8f6c\u6362\u4e3a\u6574\u6570
    \n label = f\\’Pedestrian {conf:.2f}\\’
    \n cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
    \n cv2.putText(frame, label, (x1, y1 – 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)<\/p>\n

    # \u663e\u793a\u7ed3\u679c
    \n cv2.imshow(\\’Pedestrian Detection\\’, frame)<\/p>\n

    # \u6309 \\’q\\’ \u952e\u9000\u51fa
    \n if cv2.waitKey(1) & 0xFF == ord(\\’q\\’):
    \n break<\/p>\n

    # \u91ca\u653e\u6444\u50cf\u5934\u8d44\u6e90
    \ncap.release()
    \ncv2.destroyAllWindows() <\/p>\n

    \u4ee3\u7801\u8bf4\u660e<\/h4>\n
  • \u52a0\u8f7d\u6a21\u578b\uff1a\u4f7f\u7528 attempt_load \u51fd\u6570\u52a0\u8f7d\u9884\u8bad\u7ec3\u7684 YOLOv5 \u6a21\u578b\u3002<\/li>\n
  • \u8bbe\u7f6e\u6444\u50cf\u5934\uff1a\u4f7f\u7528 cv2.VideoCapture(0) \u83b7\u53d6\u9ed8\u8ba4\u6444\u50cf\u5934\u7684\u89c6\u9891\u6d41\u3002<\/li>\n
  • \u56fe\u50cf\u9884\u5904\u7406\uff1a\u4f7f\u7528 letterbox \u51fd\u6570\u5c06\u56fe\u50cf\u8c03\u6574\u4e3a\u6a21\u578b\u8f93\u5165<\/li>\n","protected":false},"excerpt":{"rendered":"

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