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用TensorFlow Lite在树莓派上部署目标检测

news 2026/4/22 12:57:31

安装TensorFlow Lite和依赖库

在树莓派上运行以下命令安装TensorFlow Lite和相关依赖库：

sudo apt-get update sudo apt-get install python3-pip pip3 install tflite-runtime pip3 install opencv-python-headless

下载预训练模型

从TensorFlow官方模型库下载适用于TFLite的目标检测模型（如SSD MobileNet）：

wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip unzip coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip

编写推理代码

创建Python脚本（如detect.py）加载模型并执行推理：

import cv2 import numpy as np import tflite_runtime.interpreter as tflite # 初始化模型 interpreter = tflite.Interpreter(model_path="detect.tflite") interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() # 处理输入图像 def run_inference(image): input_data = cv2.resize(image, (300, 300)) input_data = np.expand_dims(input_data, axis=0).astype(np.uint8) interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() boxes = interpreter.get_tensor(output_details[0]['index']) classes = interpreter.get_tensor(output_details[1]['index']) scores = interpreter.get_tensor(output_details[2]['index']) return boxes, classes, scores

实时摄像头检测

添加摄像头捕获和结果可视化代码：

cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() boxes, classes, scores = run_inference(frame) # 绘制检测结果 for i in range(len(scores[0])): if scores[0][i] > 0.5: ymin, xmin, ymax, xmax = boxes[0][i] cv2.rectangle(frame, (int(xmin*frame.shape[1]), int(ymin*frame.shape[0])), (int(xmax*frame.shape[1]), int(ymax*frame.shape[0])), (0, 255, 0), 2) cv2.imshow('Detection', frame) if cv2.waitKey(1) == ord('q'): break cap.release() cv2.destroyAllWindows()

性能优化技巧

启用树莓派GPU加速（需安装libedgetpu）：

sudo apt-get install libedgetpu1-std

使用量化模型减少计算量，调整输入分辨率平衡精度与速度。对于多线程处理，可结合Python的multiprocessing模块。

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模型自定义训练

如需使用自定义数据集：

  1. 使用TensorFlow Model Garden训练模型
  2. 通过TFLite Converter导出为.tflite格式
  3. 修改标签文件适配新类别

训练命令示例：

python object_detection/model_main.py \ --pipeline_config_path=ssd_mobilenet_v1_coco.config \ --model_dir=training/ \ --alsologtostderr
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